The Race for the Perfect Robotic Hand

Comparison of Robotic Hands from Commercial Humanoid Platforms and Hand Manufacturers

Introduction

Humanoid robots are on the verge of leaving research labs and entering the real world – and their hands may be the final, crucial piece of the puzzle. By the end of 2026, several serious manufacturers plan to commercialize humanoid robots capable of useful work. A robot’s hand largely determines what jobs it can do: the human hand is incredibly versatile, with over two dozen joints and rich sensory feedback, letting us thread a needle or heft a heavy box. Replicating that dexterity and sensitivity is hard. In fact, Elon Musk has called the human-like hand “Tesla’s hardest problem,” noting that a human hand has about 27 degrees of freedom (independent axes of motion) and reproducing its versatility takes tremendous engineering effort. This article compares the robotic hands of leading humanoid robots expected to be in production by 2026. We’ll look at each company’s background, the design of their robot’s hand, and how it stacks up in dexterity, cost, intended usage, degrees of freedom, unique innovations, sensors, strength, and other notable features – and finally, how these mechanical hands compare to the gold standard: the human hand itself.

Tesla Optimus: Auto-Scale Manufacturing Meets Dexterous Design

Tesla – best known as an electric car pioneer – entered the humanoid robot race with its Optimus robot. Leveraging expertise in batteries, motors, and mass production, Tesla aims to eventually build humanoid robots in high volume at (relatively) low cost. Optimus was first revealed in 2022, and Tesla has iterated quickly on its design. Notably, the hand of the Tesla Bot has seen a major upgrade: the first prototype hand had only 11 degrees of freedom (DOF), but the latest generation features a 22-DOF hand – nearly doubling its dexterity. For comparison, a human hand has around 27 independent DOFs in the fingers. Tesla’s new hand design is thus approaching human-level articulation, a big leap from simpler claw-like grippers on many earlier robots.

Tesla’s Optimus robot catching a thrown tennis ball with its new 22-DOF hand. Greater dexterity (22 moving joints in the hand) allows delicate tasks like this that earlier 11-DOF hands could not handle.

Dexterity & Technology: Observers have noted Optimus performing fine tasks like picking up tiny objects and even catching a ball mid-air with its new hand. The hand’s dexterity is enabled by a tendon-driven mechanism similar in concept to human tendons: electric motors located in the robot’s forearm wind cables that run through the wrist into the fingers. According to a Tesla patent, the system uses miniature planetary gearboxes and ball screws to convert motor rotation into linear tendon motion, pulling on finger “bones” to bend joints. Springs along the tendons (much like bicycle brake cables) keep them taut through wrist motions. This clever engineering achieves a compact, human-like hand with five fingers. Each finger on Optimus can move in multiple segments, mimicking the phalanges of a human finger. With 22 DOF just in the hand (and an additional three in the wrist/forearm), Optimus’ hand has nearly the kinematic complexity of a human’s. In demos, it can grasp fragile items or perform precision pinch grasps. Tesla’s team acknowledges that scaling up manufacturing of such a complex hand is an enormous challenge – Musk quipped that mass-producing the hand “is 100 times harder than [just] designing it” and that Tesla had to custom-design every tiny actuator and gearbox to meet the requirements. The expected payoff is a highly capable general-purpose manipulator that Tesla can build at automotive scales, driving costs down over time.

Cost & Manufacturing: While Tesla hasn’t released a price for Optimus, the company’s strategy is to apply automotive mass-production techniques for affordability. Tesla is uniquely positioned to manufacture motors, sensors, and battery systems in-house. By designing the hand’s actuators from scratch and simplifying parts where possible, they aim for a cost advantage. Musk has hinted that Optimus could eventually cost under $20,000, though early units would be far more expensive to build. One cost-driven design choice was to use underactuation – some of the 22 joints are not driven by their own motor but move via linkages. This reduces the total motor count (and cost) while still giving the hand human-like motion. Tesla’s focus on manufacturability is evident: the hand’s mechanisms are packed into a slim forearm and lightweight fingers, keeping the hand about the size and weight of a human’s (the human hand is only ~0.6% of body mass, and Optimus follows a similar proportion). By leveraging its supply chain and engineering economies of scale, Tesla hopes to bring the per-unit cost down significantly once they ramp up production in their “robotics” factories.

Intended Usage: Tesla initially envisions Optimus as a worker for menial, repetitive, or dangerous tasks in factories and warehouses. The hand is designed to be versatile enough for tasks like picking up assorted parts, manipulating tools, fastening screws, or packaging goods – essentially chores that human hands currently do on assembly lines or in logistics. A recent video showed Optimus using its upgraded hands to perform delicate factory tasks (e.g. sorting objects, assembling components) with precision. In the future, Tesla expects these robots to take over dull household chores as well, so the hand is meant to handle a wide range: from grasping a lightweight egg without cracking it to carrying a heavy load. In terms of raw strength, the Optimus hand (backed by powerful forearm actuators) can apply significant force – Tesla hasn’t published exact numbers, but the hand has been shown lifting moderately heavy objects and could likely crush something like an aluminum can if directed. At the same time, the fingers are sensitive enough for fine motor tasks. Tesla is training Optimus using human-collected data (teleoperating robots to perform tasks) so that the robot hand learns skilled maneuvers. With its five-fingered, human-like layout, the Optimus hand is inherently general-purpose rather than niche – it’s not specialized for one task, which aligns with Tesla’s broad ambitions for its humanoid.

Degrees of Freedom: The 22 DOF in the hand far exceeds most competitors at present. (For context, Boston Dynamics’ research humanoid Atlas uses simple clamp grippers instead of articulated fingers, and many commercial robots stick to 2 or 3 DOF grippers.) This means Optimus can move each finger in multiple ways – likely 3 joints per finger as in a human (aside from the thumb, which has extra mobility). More joints mean more degrees of freedom to conform the hand to objects, but also more complex control. Tesla’s jump from 11 to 22 DOF was a deliberate decision to pursue near-human dexterity, where others often settle for simpler hands. It does increase complexity (and failure modes), but Tesla believes advanced AI and control can handle it. The benefit is clear in tasks like catching a ball – earlier 11-DOF hands couldn’t coordinate fingers to cradle a fast-moving object, but the 22-DOF hand can form a better “net” shape to absorb the catch. Tesla appears confident that the added DOFs will unlock far more tasks, outweighing the downsides of complexity.

Unique Innovations: One special aspect of Tesla’s hand design is the use of flexible tendons and springs for finger actuation. This gives the hand a form of compliance – it isn’t completely rigid. Much like human tendons and muscles stretch a bit under load, the Optimus tendons (with spring buffers) can absorb shock and maintain tension. This helps the robot grip objects securely and even sense when it’s touching something. Tesla also integrated tactile sensing into the new hand: the company noted “enhanced tactile sensing” in the fingers, likely meaning there are pressure sensors at the fingertips or along the fingers. Combined with vision, this allows the robot to adjust grip force dynamically (firm for a heavy tool, gentle for a delicate object). Another unique trait is Tesla’s AI approach – Optimus is backed by the same kind of neural-network learning that Tesla uses for self-driving cars. The robot’s control AI uses large datasets of human hand movements and object interactions to refine its skills, effectively learning from human demonstration at a massive scale. In sum, Tesla’s hand is special for its human-like design philosophy (five fingers, multi-jointed) combined with Tesla’s prowess in sensors, AI, and manufacturing. Few others have attempted such an all-round human mimic at scale.

Fingers & Form: Optimus has the full set of five fingers (thumb + four). Tesla didn’t eliminate any digits – they want full human equivalence. Some robots simplify by dropping the pinky finger, but Tesla kept it, likely because the pinky contributes significantly to grip strength and stability (in human hands, the pinky is surprisingly important for a strong grip). The fingers are proportioned like a human’s and can spread apart (finger abduction/adduction) to some degree, giving a wide grasp span. Each finger’s joints are presumably driven by the forearm motors via tendons. The decision to go with five fingers (as opposed to, say, a three-finger gripper) was driven by Tesla’s goal of maximal versatility – five-finger hands can perform virtually all the grasps a human can. It does make control harder, but Tesla is tackling that through advanced software.

Sensors and Feedback: As mentioned, the new Optimus hand incorporates sensors for touch and proprioception. Internal encoders track each joint’s position and force, providing feedback to the control system. On the external side, Tesla is likely using tactile pads or force sensors on the fingertips to detect contact pressure. Elon Musk has mused that an ideal robot hand should sense not just pressure but also temperature and shear (sliding force) to truly mimic human touch. While it’s unclear if the current Optimus hand measures temperature, it does have improved tactile feedback compared to the first prototype. This helps it handle fragile objects (it can detect when it’s just starting to squeeze an egg, for instance, and stop before cracking it). Vision-wise, Optimus relies on external cameras in its head and body (not cameras in the hand), but fine hand-eye coordination is achieved by calibration between what the cameras see and how the hand moves. The electronic skin concept – artificial skin with embedded sensors – is something Tesla is surely considering (rival projects in China already tout “electronic skin” on robot hands). For now, Optimus’s fingertips likely have pressure sensor arrays for grip force control.

Strength and Delicacy: The Optimus hand strikes a balance between power and finesse. Tesla hasn’t published exact grip strength, but given the size of the actuators and the leverage of tendons, each finger can probably exert dozens of Newtons of force – enough to crush a beverage can or firmly grip a power tool. The hand as a whole can lift a substantial weight; combined with the arm, Optimus can reportedly lift on the order of 20+ pounds per arm. It’s designed to carry objects like factory parts, grocery bags, or small furniture. At the same time, the control system allows very gentle handling. Videos have shown Optimus picking up an egg without breaking it and handling small components like screws. The tendons and springs in the fingers naturally impart some compliance, acting like shock absorbers so that minor collisions or over-squeezes don’t instantly spell disaster. A great example of strength plus delicacy is the tennis ball catch demo: the hand closes fast and firmly enough to grab a ball flying through the air, but then immediately relaxes grip slightly so as not to squish the ball and to cushion the catch. This demonstrates fine force control.

Notable Features: One intriguing note about Optimus is that Tesla is only just beginning to exploit the hand’s potential. Initially, they teleoperated the robot to test the hand (e.g. a human remotely controlling Optimus to catch balls), and they achieved almost zero-latency responsiveness – a technical feat showing the hand and software can respond as fast as a person’s reflexes. Over time, Tesla will move from teleoperation to full autonomy as the AI “brain” learns. Another notable aspect is modularity: Tesla’s hand is part of a whole limb system, but conceivably they could offer the hand design for other uses (Musk hinted the Optimus limbs could work with brain implants for disabled patients). This opens the door to advanced prosthetics or integration with other robotic systems. Finally, Tesla’s extensive data collection (they mention a “human data collection farm” for training Optimus) is something unique – basically teaching the hand by example, which could give it a big lead in real-world dexterity over competitors that rely purely on pre-programmed motions.

Agility Robotics (Digit): A Workhorse with Simple but Effective Hands

Agility Robotics, an American startup spun out of academic research at Oregon State University, has been working on legged robots for over a decade. Their humanoid Digit is already one of the first commercially deployed humanoids in real workplaces (pilots in warehouses). Agility’s roots are in building robust bipedal machines like Cassie (a leg-only robot); with Digit they added arms and hands to enable manipulation tasks. Digit’s design philosophy prioritizes reliability, efficiency, and practical utility over replicating the full human form. This is evident in its original hand design: earlier versions of Digit used relatively simple “flat, spatula-like” gripper hands instead of multi-fingered human-like hands. Those grippers were essentially two-finger clamps – very durable and good at holding boxes, but not dexterous. However, Agility has since redesigned Digit’s hands to have five fingers each, improving its dexterity for new tasks. The latest generation of Digit (unveiled around 2023–2024) features two five-fingered robot hands that the company claims are capable of near human-level dexterity. This was a significant upgrade intended to expand Digit’s usefulness beyond just lifting totes.

Dexterity & Design Choices: While Digit’s new hands have five fingers like a human, Agility made some deliberate simplifications. According to one analysis, Digit’s hand “doesn't have the phalanges system used by Tesla Optimus”. In other words, each finger on Digit may not have as many joints (or independent motions) as a human finger or Tesla’s fingers. The hand likely has on the order of 10–16 DOF in total – less than Tesla’s 22 DOF hand. For example, Digit can flex its fingers to grip objects, but it might not individually bend the last knuckle of each finger the way a human can. Why would Agility accept lower DOF? Because their target tasks (like lifting boxes, handling totes) don’t require extremely fine finger gaiting. Fewer joints means a simpler, more robust hand that’s easier to control and cheaper to produce. Agility’s CTO Jonathan Hurst has noted that for the current use cases, an ultra-dexterous hand isn’t strictly needed – what’s needed is a hand that can grasp common objects reliably and withstand heavy use. So Digit’s fingers are designed more like sturdy hooks that can also do basic manipulations, rather than ultra-articulated human replicas. That said, Agility’s five-finger hands can perform quite a variety of actions: they can pinch, grip, and hold objects of varied shapes, and even do things like turning a doorknob or pressing buttons. They demonstrated tasks such as locking a door and grasping tools, which earlier grippers couldn’t do. Digit’s dexterity is likely a bit lower than Optimus or Sanctuary (discussed later), but it’s good enough for many industrial and logistics jobs.

Cost & Manufacturing: Agility Robotics is very focused on making Digit commercially viable at scale. They even built a 70,000-square-foot “RoboFab” factory in Oregon to mass-produce Digits, aiming for 10,000 units per year at full capacity. This focus on scalability influenced the hand design. By using simpler hands (with fewer actuators), Agility keeps the robot’s cost and maintenance lower. Each Digit hand currently might have only a handful of motors (possibly driving multiple fingers via linkages). For example, the index and middle finger might be coupled together in movement, rather than each having separate multi-joint articulation. This reduces part count. The trade-off is less individual finger finesse, but it makes the hands more robust and cheaper to build. Agility has also hinted that their end-effectors (hands) are modular – they can be swapped out for different tools. In the future, a warehouse might fit Digit with a specialized gripper for one task and a five-finger hand for another. This modular approach is cost-effective because the base robot remains the same. In terms of manufacturing technique, Agility leverages a lot of off-the-shelf components (motors, sensors) integrated with their custom designs. The hands likely incorporate 3D-printed or molded components for finger skeletons and use standard servo motors or geared motors for actuation. Nothing too exotic – which is intentional to keep costs manageable for clients like e-commerce warehouses that may deploy fleets of Digits.

Intended Use Cases: Digit is unapologetically designed for warehouse and industrial work – tasks like moving and lifting objects, order fulfillment, and material handling. Its hands reflect that: they excel at picking up boxes, totes, and other containers, as well as single items like parcels. With the latest five-finger hands, Digit can do more “human-like” odd jobs around a facility – for instance, unstacking bins, stocking shelves with individual products, or using simple hand tools. Agility mentions use cases such as unloading trucks, moving packages, palletizing (stacking) boxes, and even basic installations or inspections. The design of the hand – strong grip, moderate dexterity – is tailored to these functions. Digit’s hands can grip up to 35 pounds (about 16 kg) in a pinch grasp, sufficient for most boxes in a warehouse. In a home setting (if Digit were ever used there), the hands would be capable of chores like carrying groceries, operating appliances, or picking up clutter. However, very delicate tasks (like cooking or folding laundry) might be a stretch for Digit’s current hands, since those require fine finger control and complex in-hand object manipulation.

Degrees of Freedom: While Agility hasn’t publicly detailed the DOF count of the newest hands, an educated guess from their statement of “human-level dexterity” is that each hand has multiple DOFs per finger but not as many as a human. Perhaps each finger has 1 or 2 joints that move (instead of 3 in a human finger), and the thumb might have 2 DOFs. This could total around 10–15 active DOFs plus some passive adaptability. For instance, the fingers may be spring-loaded to passively conform to an object’s shape when grasping. Agility consciously chose fewer DOFs than competitors like Tesla to prioritize reliability. Does this limit Digit’s use cases? Potentially, yes – Digit might not be able to, say, tie shoelaces or play the piano, tasks that require independent control of every fingertip. But those tasks aren’t needed in a warehouse. Agility’s bet is that 99% of industrial tasks can be done with a simpler hand, and the cost savings are worth it. In scenarios where ultra-fine dexterity is required, they could always develop a more complex hand later or partner with a company that makes dexterous end-effectors. In fact, their architecture allows end-effector swapping, as noted. So degrees of freedom can be “upgraded” in the future if needed, without redesigning the whole robot.

Unique Features: One special thing about Digit’s hands is how they integrate with the whole-body approach to manipulation. Digit’s arms and hands work in concert with its legs for stability. For example, the arms (with hands) double as balancers – Digit can use arm motions to counterbalance while walking or lifting, much as a human would. This means the hands aren’t just dangling end-effectors; they are considered part of the dynamic control system. Another notable aspect: Agility has included some compliance in the hand design. The fingers likely have a bit of flex (materials or mechanism) to tolerate misalignments. This “forgiving” nature is unique compared to rigid metal hands; it helps Digit grab irregular objects without precise alignment. Also, Agility has emphasized safety in human-robot interaction – Digit’s new hands and arms have padding and are comparatively lightweight, so if the hand bumps a person, it’s less likely to cause injury. In terms of inventions, Agility’s prior work on prosthetic and robotic legs introduced some innovative transmissions; they may have applied similar principles in the hand actuators for efficiency. One could also say simplicity is their innovation: by forgoing the full complexity of a human hand, they innovated in making a hand that is just complex enough. Digit’s hands are a bit of a contrast to others: where Tesla and others aim for maximal biomimicry, Agility takes a pragmatic engineering approach – and in doing so, they actually got a product to market first (Digit is one of the first humanoids sold and deployed).

Fingers and Configuration: The current Digit hand has five fingers per hand, matching the human count. However, it’s possible that not all five are fully independent. For instance, the ring and pinky finger might move together as one unit (a common approach in many robotic hands, since those two fingers often act together for power grips). The thumb opposes the other four, allowing a variety of grasp types (cylindrical grasp, pinch, etc.). Early versions of Digit had only two broad “fingers” that formed a claw; moving to five narrower fingers greatly increases the kinds of objects it can handle – from small tools to oddly shaped parts. Agility likely retained all five digits because some tasks, like turning a knob or typing a keypad, are much easier with a thumb and at least two fingers (index and middle), and the extra ring/pinky provide more grip surface for heavy objects. By not dropping the pinky, Digit can achieve a more human-like secure grasp on thick objects (the pinky and ring finger together add a lot of grip stability – try holding a jar with just two fingers vs all four!). So although Digit’s fingers are simpler internally, the external form being human-like is important for interfacing with the human environment (tools, handles, etc., are designed for five fingers).

Sensors: Agility Robotics hasn’t highlighted exotic tactile sensors in Digit’s hands – at least not publicly. The focus seems to be on robust perception via vision (cameras and LiDAR) and proprioception (internal joint sensors). The hands likely have basic force sensing – for example, current sensors on the hand motors can infer how hard the grip is (a form of force feedback). There may also be contact sensors on the fingertips or palm to detect when an object is touched. But as of now, no “electronic skin” or high-resolution tactile array has been mentioned for Digit. This is in line with their philosophy: many warehouse tasks can be done with vision guidance and simple force control (e.g., know when you’ve gripped a box by a slight increase in motor torque). One advantage of a slightly compliant hand is that it naturally provides some feedback; if the fingers are springy, they will wrap and hold an object with a certain force without precise sensor input. That said, Agility is certainly aware of advanced sensing – the company is building up an AI “skill library” for Digit, and in time they might add more sensors to enhance manipulation skills. The current Digit model, though, probably doesn’t measure temperature or texture, etc. It might rely on vision to identify objects and then use a programmed grip force appropriate for that object type (soft vs hard, light vs heavy). In short, Digit’s hands are low on frills, high on function.

Strength and Capability: Each of Digit’s gripper-hands is quite strong – as noted, they can lift about 35 pounds together (e.g. a 35-lb box with two hands, or smaller loads one-handed). This is roughly the lifting capacity of a fit human without straining. It’s sufficient for most warehouse cartons and even some tools or components. The fingers, being sturdy, can handle rough use: they won’t shatter if Digit bangs a hand against a shelf or drops a box on it. However, they are not intended for precision force like turning a tiny screw – they lack that fine control. In terms of delicacy, Digit can certainly pick up something like a coffee mug or an egg, but it may need to be careful – without extensive tactile sensors, it relies on pre-set grip forces. Agility has likely tuned the grip strength so that when an object is detected, the hand closes until a certain motor torque threshold is met. This works for many items but can occasionally misjudge (e.g., a foam object might get squeezed more than a solid one). Still, videos have shown Digit handling fragile objects successfully, indicating its control is sufficiently refined for gentle tasks. Digit’s upper-body and hand design together let it do “human-level” work to a degree – for instance, it can grab a bin and precisely position it on a conveyor, or reach up to push a button. The range of motion of the fingers might not allow, say, intricate finger gaiting (re-grasping an object within the hand), but it can always re-position by setting the object down and picking it up differently using two hands if needed. One notable feat: Agility’s latest Digit was demonstrated autonomously unloading plastic totes and placing them on shelves, an application that needs reasonably precise hand coordination and force control (so as not to drop or crush the tote). The fact that Digit can perform this reliably in a live demo speaks to the hands being strong and reliable enough for repetitive work.

Notable Features: Agility Robotics has a philosophy of “using the right tool for the job” – in fact, they often refer to the hands as “end effectors” and suggest that different tasks might use different end-effector designs. The current five-finger hand is a generalist tool, but Digit could just as easily be fitted with, say, a vacuum suction gripper for certain picks, or a power tool attachment. This flexibility is noteworthy because it means Digit’s identity isn’t tied to a single hand design. Agility is pragmatic: if a specialized hand can do a job better, they can swap it in. Another interesting aspect is that Digit’s hands contribute to non-verbal communication – the newest version of Digit has LED “eyes” and even a head to convey intent, but the arms and hands also help signal the robot’s status. For example, Digit might hold its arms in a neutral open position when walking to appear non-threatening to nearby humans. The five-fingered hands, being more human-like, could play a role in gestures or signals (waving, pointing). Simpler grippers couldn’t do that. Also, the safety systems integrated into Digit mean the hands will stop if they encounter unexpected resistance (to avoid pinching a person, for example). This is important for collaborative environments. In summary, Agility’s Digit hand may not be the most dexterous in an absolute sense, but it is battle-tested in real workplaces and strikes a smart balance between capability and simplicity. It’s a hand designed to get actual jobs done with minimum fuss – and Digit’s early commercial deployments prove the wisdom of that approach.

Sanctuary AI (Phoenix): Mini-Hydraulic Hands for Fine Manipulation

Sanctuary AI is a Canadian company (based in Vancouver) taking a bold approach to humanoid robots. Co-founded by pioneers in AI and robotics (one founder, Geordie Rose, previously co-founded the quantum computing firm D-Wave), Sanctuary’s mission is to create general-purpose robots with human-like intelligence. Their humanoid robot, recently unveiled as Phoenix, is focused on accomplishing a very wide range of tasks – essentially anything a person can do with their hands. To achieve this, Sanctuary has developed arguably one of the most dexterous robotic hands in the industry. Sanctuary’s latest robotic hands boast 20–21 degrees of freedom (comparable to a human’s ~25 DOF) and are richly instrumented with sensors, giving them fine motor skills and a sense of touch. What really sets Sanctuary’s hand apart is its actuation technology: instead of electric motors and cables, it uses miniaturized hydraulic actuators (tiny fluid-powered valves and pistons) to move the fingers. This innovative design yields extremely high power density – meaning the hand can be very strong and fast for its size, yet still compact.

Dexterity & Fundamental Tech: Sanctuary’s hand is designed to rival human hand dexterity. In practical terms, this means each finger of the hand has multiple joints and can move independently and in coordination for complex maneuvers. Sanctuary has demonstrated that their robot can do in-hand manipulation, a challenging skill where an object is repositioned within the hand (think rolling a pen between your fingers). This requires a high level of finger control and coordination. The hand has 21 DOF in total (likely distributed as four joints in the thumb and 3 joints in each of the other four fingers, similar to a human). Importantly, these joints are actively controlled – Sanctuary’s unique micro-hydraulic valves allow even the small joints at the fingertips to be powered and precisely controlled. The use of hydraulics is a deliberate choice: fluid power can deliver greater force in a small package compared to electromagnetic actuators. Sanctuary claims their hydraulic system offers an order of magnitude (10x) higher power-to-weight ratio than cable-driven or electric hands. This means the hand can be both strong and nimble – it can squeeze hard or move swiftly as needed. Hydraulics also provide inherent compliance (the fluid has slight give and the system can modulate pressure finely), which is excellent for controlling grip force delicately. Sanctuary’s hand can, for example, firmly tighten a bolt with a tool and immediately switch to gently picking up a fragile item. The mini hydraulic pumps and valves are engineered small enough to fit into the robot’s forearms and hands, a remarkable feat of miniaturization. They reported testing these tiny valves through 2 billion cycles without leaks or failure, indicating reliability – a common concern with hydraulics (leaks) has been aggressively addressed. In summary, Sanctuary’s hand technology is pushing the envelope by combining human-like kinematics (DOF) with high-performance actuation, aiming for no compromise in dexterity.

Cost & Manufacturing: Using hydraulics and custom precision components likely makes Sanctuary’s hands expensive, at least in the short term. Sanctuary is less focused on immediate mass production and more on showcasing capability – their strategy involves deploying a limited number of robots in pilot programs to prove utility, then scaling up. The cost is justified if the hand can perform many tasks (replacing human labor in critical roles). Sanctuary’s founders have a high-tech background, and their investor list includes tech luminaries, so they have capital to invest in R&D-heavy approaches like custom hydraulics. Over time, they might reduce costs by refining the manufacturing of those micro-valves and perhaps using economies of scale (if they partner with, say, an industrial hydraulics manufacturer). One cost advantage of their approach is that one robot could potentially do the job of many specialized machines – because the hands are so versatile. For example, instead of separate robotic tools for sorting, assembly, etc., a single Sanctuary general-purpose robot can be re-tasked to different jobs simply by changing its programming (the hands are general enough). That flexibility might offset the high unit cost by multiplying the robot’s usefulness. Sanctuary has also made the hand modular – it’s designed in a way that it could be attached to other robotic arms in the future. If they ever decided to sell the hand as a standalone product, it could find a market in other robotics platforms that need dexterity (which could drive up production volume and drive down cost per unit). Still, at least until 2026, Sanctuary’s hand will be a premium, low-volume component handcrafted for their own robots. They are essentially at the cutting edge, not yet at the commoditization stage.

Intended Usage: Sanctuary explicitly targets “general purpose” tasks – basically, anything a human hand can do in an industrial or commercial setting, they want their robot to do. They famously tested their prototype in a retail store where it performed 110 different tasks, from tagging merchandise to cleaning to moving products. The hand is built to handle fine manipulation: turning keys, typing on keyboards, picking up tiny objects like coins, using tools (e.g. screwdrivers, wrenches), and doing assembly tasks. Sanctuary’s vision is that their humanoids can work in factories, laboratories, or even serve in biomedical or space applications where delicate yet versatile hands are needed. One scenario they mention is assisting with tasks that require a human’s fine motor skills – for instance, assembling small devices, packing sensitive materials, or operating machinery controls designed for humans. Because the hands have such fidelity, Sanctuary’s robot could also do complex chores in a home or office: cooking preparation (handling utensils, cutting vegetables), fixing gadgets, or sorting and manipulating a wide variety of objects. Another niche is teleoperation for dangerous tasks: a human could remotely control Sanctuary’s hands (with haptic feedback) to defuse a bomb or handle hazardous materials, essentially using the robot as an avatar. Sanctuary did initially teleoperate their system to train it and gather data. Over time, their AI (“Carbon” is the name of their control AI) is learning to operate autonomously. But teleoperation remains a use-case – e.g., a skilled human could guide the robot in performing an intricate new task, and the robot hand is capable enough to carry it out exactly. In short, Sanctuary’s hands are aimed at maximal versatility, so the market niches span from factories to retail to labs to hazardous environments – anywhere you would wish you could send a human-like pair of hands to get a job done.

Degrees of Freedom: With ~20–21 active DOF in the hand, Sanctuary’s design is neck-and-neck with Tesla’s in terms of raw kinematics. Each of the five fingers has multiple joints: the thumb likely has four (to allow opposition and rotation), and the other fingers perhaps three each. They also incorporated finger abduction/adduction – the fingers can spread apart or together (that’s often counted as an extra DOF per finger or for the group). Jim Fan, an AI researcher who commented on Optimus, noted that finger abduction (the ability to move fingers laterally) is a feature Sanctuary’s hand has which many others lack. This is important for in-hand manipulation: spreading fingers to reposition an object. Additionally, Sanctuary’s wrist is very dexterous (the arm has a 7-DOF design including a wrist with at least 2-3 DOF). All told, the hand plus wrist can achieve most orientations a human hand can. They did mention “20 in total” in one press release and “21” after a tactile sensor integration – possibly the extra DOF came from an improved thumb or an extra degree in the wrist. Regardless, it’s in the same ballpark as a human (which has 21 DOF if you exclude the palm’s minor movements, or ~27 if you count everything). This means Sanctuary’s hand can do nearly any configuration a human hand can, from fist to flat to a variety of precision grips. The high DOF count does mean controlling it is complex – Sanctuary is leveraging advanced AI and also having humans teleoperate to teach the robot. But the benefit is they did not need to compromise on what tasks the hand can attempt. If a human can physically do it with a hand, their robot likely can too (strength permitting).

Unique Innovations: The standout innovation is the mini hydraulic actuators. Traditional hydraulics (as seen in big industrial robots) are powerful but bulky. Sanctuary created tiny valve systems that fit in a humanoid’s limbs, which is unprecedented. These valves control fluid flow with such precision that the fingers can make rapid, fine adjustments – giving speed, strength, and smooth control simultaneously. Hydraulics also offer high force at low speeds, which is great for gripping strongly without large motors. Additionally, the hand incorporates proprietary haptic sensors – Sanctuary says they have technology that “mimics the sense of touch”. This likely includes arrays of pressure sensors on the fingertips and possibly across the fingers and palm. They might also measure vibration (to detect slipping) and temperature if needed (for instance, distinguishing a hot surface). By fusing data from these sensors, the robot’s AI can get feedback akin to a human’s nerve receptors in the skin. Sanctuary’s approach to AI is also unique: they pair the physical robot (Phoenix) with an AI “mind” (Carbon). Carbon is trained in part by sensorizing human workers – they actually have humans wear gloves and suits that record motions and forces as they do tasks. This data is used to teach the robot how a skilled human uses their hands to accomplish things. It’s a very interesting human-in-the-loop training strategy. Another unique aspect: Sanctuary isn’t afraid to pack cutting-edge tech into their robot without waiting for it to be cheap. For example, they integrated the latest tactile sensor arrays into the Phoenix hand in 2024, claiming the robot now has an advanced sense of touch across the hand. This underscores that they see the hand as the key to unlocking general usefulness, and they are throwing the kitchen sink at it – hydraulics, sensors, AI, whatever it takes. The result is arguably one of the most biomimetically advanced hands to be slated for real-world deployment.

Fingers & Configuration: Sanctuary’s hand is a full five-finger design, matching human anatomy in layout and proportion. Unlike some others, they did not drop the pinky or any finger – because for general tasks, every finger has a role. The thumb is opposable, meaning it can touch the tip of each other finger, enabling precision pinches and power grips. One thing Sanctuary has shown is the ability to do fine finger gaiting: e.g., rotating an object within one hand using the fingers. This often requires coordinated use of all five fingers – the pinky and ring might temporarily hold an object while the thumb and index reposition, etc. Their four-finger designs (like some competitors use) can’t do that as effectively. Sanctuary’s decision to include all five fingers (even though that adds extra actuators and complexity) stems from their general-purpose goal. For instance, try to tie your shoelaces without using your pinky – it’s possible but more awkward; with a fully human-like hand, it’s straightforward (for a trained human anyway!). The physical appearance of the hand is roughly human-sized as well (their robot is 5’7” tall, so the hands are similar to an average adult’s). This means all the tools and objects made for human hands can be grasped. The fingers have a lifelike range of motion: the knuckles bend, the middle joints bend, and the fingertips can curl. Finger spread (abduction) was specifically mentioned, which is crucial for actions like typing or grasping wide objects. In effect, Sanctuary’s fingers appear to replicate the full complement of human finger motions. The use of four fingers plus thumb also implies they recognized that a four-finger (thumb + three) arrangement like some robot hands use (shadowing the fact that humans rarely use the pinky independently) still isn’t as good in some grasps – e.g., a cylindrical grasp on a thick pipe is stronger and more stable with the pinky involved. Thus, they went for the whole five.

Sensors and Feedback: Sanctuary’s hand is bristling with sensors. Internally, every joint has position sensors and force feedback (via hydraulic pressure sensors). Externally, Sanctuary has integrated tactile sensors into the fingertips and possibly the palm. These tactile sensors likely measure pressure distribution – allowing the robot to feel how an object is contacting the hand (e.g., is it pinched at the tip or nestled in the palm?). They also mentioned “proprietary haptic technology that mimics the sense of touch”, which suggests a combination of tactile and force sensing that gives the control system a rich input similar to human touch receptors. For instance, a human hand can feel an object’s texture and slip; Sanctuary’s hand may have high-frequency vibration sensors to detect if an object is slipping from its grip, prompting an automatic grip adjustment. Temperature sensors could be present too, though tasks rarely require temperature feeling (except to avoid hot objects – a robot could use an IR sensor for that). Proprioception is handled via joint encoders and hydraulic pressure feedback, meaning the robot knows how bent each finger is and how much force it is exerting. The integration of vision and touch is also a key aspect – the robot’s cameras identify objects and guide the hand to them, then the tactile sensors take over to adjust grip when contact is made. This multi-modal sensing is exactly how humans operate (eyes to roughly position, touch to finalize grip), so Sanctuary’s approach is quite biomimetic. One could say Sanctuary’s hand is moving toward an “electronic skin” ideal, where the robot can feel all the things a human hand can: pressure, shear (sliding force), and maybe even more (like using tiny cameras in fingertips for detailed view – some research hands do that, though Sanctuary hasn’t said they do). The combination of advanced actuation and sensing is why observers call Sanctuary’s hand industry-leading in dexterity.

Strength and Precision: Despite being delicate enough to manipulate small objects, Sanctuary’s hand is also fairly strong. Thanks to hydraulics, it can exert substantial force. The exact strength hasn’t been publicly quantified, but for context, NASA’s Robonaut 2 (an older humanoid hand that also had a dozen+ DOF) could lift about 5 kg per hand and had a very strong grip (it could squeeze >150 N). Sanctuary’s hand likely meets or exceeds that, given the power density claim. Crushing a beer can would be trivial for it – in fact, it could probably crush a can without much effort. But more impressively, it can hold an egg or a lightbulb without breaking it, thanks to fine pressure control. The speed of the hand is also notable: hydraulics can move joints very quickly. Sanctuary showed it catching objects as well, and performing rapid reconfigurations of the fingers. The idea is to have human-like speed and reaction time in the hands, so the robot can do dynamic tasks like catching, throwing, or quickly reorienting an object for inspection. Because each finger is individually strong, the hand can also do things like grasp a heavy drill and operate it (some demos from Sanctuary showed the robot handling power tools). A hydraulic system also means the hand can maintain a strong grip for long durations without motor overheating (fluid power can hold force continuously quite well). So holding a heavy object for an extended time is fine. Conversely, the precision is high – Sanctuary mentioned ±1 millimeter scale accuracy in fingertip positioning in some contexts, which aligns with being able to thread a needle or pick up a small part. One external measure of their success: Sanctuary’s robot recently set a Guinness World Record for the most AI tasks completed by a humanoid (110 tasks), many of which involved hand use. That speaks to both the versatility and performance of the hands – they weren’t specialized for one demo; they could do hundreds of distinct actions (from sweeping with a broom to choosing items from a shelf) with the same hardware.

Notable Features: Sanctuary’s approach tightly couples AI and hardware. Their “Carbon” AI learns from every task the hands perform, and improvements in software can immediately broaden what the hands can do. For example, if the AI learns a new manipulation skill (say, tying a knot), no hardware change is needed – the existing hand is capable enough. This is a noteworthy difference: some other robots might need a new tool for a new task, but Sanctuary’s generalist hand can attempt almost anything given the right software skill. Another interesting tidbit is that Sanctuary’s robot is slightly smaller and lighter than, say, Tesla’s (Phoenix is 5’7” tall, 155 lbs). Yet its hands have comparable or greater dexterity. This indicates excellent engineering optimization – packing that hydraulic system in a compact form. It also means the hands are not overly bulky; they look proportionate. The use of hydraulics also provides a kind of inherent force control – by modulating pressure, you get smooth, lifelike movement. Observers often comment that Sanctuary’s robot motions (especially hand movements) appear very fluid and human-like. In fact, there were instances where onlookers thought a person might be in a robot suit because of how natural the hand movements were. That fluidity is partly due to the fine control hydraulics allow. On the flip side, a notable challenge (which Sanctuary is addressing) is dealing with heat and noise from hydraulics – but they’ve mentioned their system has good heat managementand presumably is not too loud (the small pumps likely whir at a low volume). Another notable point: Sanctuary’s hand modules can theoretically be attached to non-humanoid systems. This hints that their tech could spread beyond just their own robot – imagine a robotic arm in a factory with a Sanctuary hand, or a mobile rover using the hand for lab work. Their modular, plug-and-play design for the hand could become a sort of gold standard dexterous end-effector in the industry, if they chose to offer it. For now, though, it gives Phoenix a distinct edge in performing the widest range of tasks. In summary, Sanctuary AI’s robotic hands are cutting-edge, aiming for human-level manipulation ability. They exemplify the philosophy that to make robots truly useful in our environments, you must solve the hand, which they call “the last frontier” of humanoid development – and they are arguably first to cross that frontier with a production-ready system.

Apptronik (Apollo): Industrial Strength and a Path to Affordability

Apptronik is a Texas-based company with roots in the University of Texas at Austin’s Human-Centered Robotics Lab (the team previously worked on NASA’s Valkyrie humanoid and other projects). Launched in 2016, Apptronik has focused on building practical humanoids for industrial and logistics applications. Their latest robot, Apollo, was unveiled in 2023 as a 5’8″, 160 lb humanoid built for warehouse work and manufacturing support. Apollo is designed with mass manufacturability in mind – Apptronik often emphasizes making a “platform” that can be produced at scale and easily maintained. When it comes to Apollo’s hands, Apptronik has taken a phased approach: the initial versions of Apollo are equipped with relatively simple grippers (minimal degrees of freedom), with the plan to upgrade to more dexterous five-fingered hands in the future as the robot’s role expands. In other words, Apollo will eventually have advanced hands, but early on it doesn’t need full human-like hands to do its intended jobs.

Dexterity & Design Strategy: For the first deployments, Apollo’s hands are essentially basic grippers – likely 1-DOF or 2-DOF claw-like hands that can open and close to grab objects like boxes or tools. This choice is intentional: Apptronik identified that many heavy labor tasks (like lifting crates, unloading trucks, carrying goods) don’t require delicate finger work, just a reliable grasping mechanism. By starting Apollo off with simpler hands (which could be thought of as interchangeable end-effectors), they reduce complexity and focus on perfecting walking, lifting, and other core capabilities. Jeff Cardenas, Apptronik’s CEO, noted that “long term, a humanoid has to have hands,” but in the short term a simple gripper suffices for the tasks Apollo is targeting. Apptronik’s roadmap includes outfitting Apollo with five-fingered dexterous hands (around 16 DOF) once those hands are fully developed and needed. In fact, some sources list Apollo’s eventual hand as having five fingers and 16 degrees of freedom, which would bring Apollo up to par with Figure or others on dexterity. But currently, Apollo might be demonstrated with, say, a two-finger pincer or a three-finger adaptive gripper. The underlying technology Apptronik brings is expertise in electric actuators and compliant control – they’ve developed custom high-performance motors (some called “elastic actuators”) for smooth force control. These actuators, when applied to a future five-finger hand, would allow precise movements and safe interactions (the compliance would prevent crushing objects or injuring humans). One could think of Apollo’s hand strategy as modularity: use the right hand for the job. For heavy lifting, maybe a stronger 2-finger clamp; for fine tasks later, swap in a humanoid hand. The fundamental architecture is built to accommodate that swap, as Apollo’s wrist likely has a standard mounting interface. So dexterity is a deferred feature – Apollo’s design can go from crude to dexterous when needed, rather than trying to do it all from day one.

Cost & Manufacturing: Apptronik has raised significant investment (hundreds of millions of dollars) with the promise of building “the iPhone of robots” – a mass-produced, general-purpose unit. Cost-effectiveness is a core goal. By initially avoiding the expense of complex hands, Apptronik keeps Apollo’s cost lower for early customers. They quoted rough price ranges of $150k–$250k for early units, which is relatively low for a humanoid (achieved partly by not over-engineering components that aren’t immediately needed). Apptronik also employs automotive manufacturing techniques and off-the-shelf parts where possible to bring costs down. For example, Apollo’s arms and legs use a lot of aluminum and carbon fiber parts designed for manufacturability. The simple gripper hands are likely 3D-printed or injection molded parts with a few standard servos – very cheap compared to a full dexterous hand with dozens of micro-motors. This gives Apptronik a potential cost advantage over rivals like Sanctuary or Tesla in the near term: Apollo can be built more quickly and cheaply because it sidesteps the hardest part (the hand). As volume grows and tasks demand it, Apptronik can invest in developing a dexterous hand. By that time, they might leverage advances made by others or falling component costs. Also, Apptronik’s heritage with NASA means they have knowledge of high-end hand designs (NASA’s Valkyrie robot had a complex hand), so they aren’t starting from scratch when they do add one. Another cost consideration: special manufacturing techniques like 3D printing for end-of-arm tools. Apptronik could allow customers to print custom gripper attachments for Apollo’s hand to suit specific tasks, rather than manufacturing many types themselves – a cost-saving approach. Overall, Apptronik’s cost strategy is to avoid any unnecessary complexity until it’s justified by a customer use-case, ensuring Apollo is financially accessible to businesses.

Intended Use Cases: Apollo is squarely aimed at logistics, manufacturing, and supply chain tasks – think of it as a strong back and arms for the factory or warehouse floor. The robot is built to do the “dirty, dull, and dangerous” work like unloading trucks, stacking heavy items, moving inventory, and working on assembly lines handling parts. In these scenarios, the current simple grippers can handle items like boxes, bins, tools, or machine parts. Apollo’s marketing shows it carrying a large tote, pushing a cart, and using power tools – all of which are doable with relatively basic grippers or tool-changers. For example, to use a drill, Apollo might simply have a bracket-like hand that holds the drill’s handle firmly. To carry a box, a clamp grip will do. As Apollo evolves, Apptronik envisions it doing more intricate tasks in manufacturing, possibly including assembly or inspection – that’s when a more dexterous hand would come in, to handle smaller components or plug in cables, etc. But initially, the focus is heavy lifting and repetitive motions to relieve human workers from injuries and fatigue. Essentially, Apollo’s role is to be a reliable robotic laborer. In a household context (if Apollo ever goes there), tasks might include carrying laundry, yard work, or fetching objects – again, things that don’t necessarily need finger-level finesse immediately. Over time, if Apollo goes into maintenance or servicing tasks, it might need to turn valves, open panels, or connect hoses, which will prompt the deployment of the upgraded five-finger hands. It’s clear Apptronik sees Apollo as a platform that can grow: release it as a powerful, simpler machine for now, then bolt on more capabilities (like better hands or improved AI) as the product matures. This way, Apollo can start generating value (and revenue) for customers with “low-hanging fruit” tasks while the higher-dexterity features are still in development.

Degrees of Freedom: The current Apollo gripper likely has 0–1 DOF (meaning it might just open/close, or even be fixed for a specific task). For instance, early demos might have Apollo with a fixed hook or a simple clamp that only has one moving joint. The arms themselves have plenty of DOF (Apollo’s whole body is around 28+ DOF), but the hand is minimal for now. However, Apptronik’s spec for Apollo’s full potential includes five-finger hands with 16 DOF in the future. That would likely break down to a thumb with 4 DOF, and three joints each on the other four fingers (4 + 4*3 = 16). That’s similar to Figure’s hand design and slightly below Tesla’s 22 DOF. It would provide enough dexterity for most human-like tasks (maybe missing some very fine motions in the palm, but covering essential finger articulation). The rationale might be that 16 DOF captures the primary movements needed, leaving out perhaps some wrist or coupling motions. If Apollo does get that hand by 2025 or 2026, it will join the ranks of fairly dexterous robots. If not, it will remain limited to simpler tasks until then. It’s an acceptable trade-off because Apollo’s main selling point in the near term is not “most dexterous,” but rather “most useful immediately in a factory”. So DOF are added only as they become necessary to unlock new jobs. This staged approach also reduces risk – fewer DOF to control initially means easier software development and safer testing. When they do add the 16-DOF hand, Apptronik will have had time to refine their control algorithms (possibly using their existing advanced arms as testing ground). Thus, Apollo’s degree-of-freedom story is one of evolution: from a few DOF in the hand to many DOF, tracking the expansion of its job repertoire.

Unique Features: One unique aspect of Apptronik’s approach is human-centric design for co-working. Apollo is designed to be “friendly” – it has a smooth, non-threatening appearance and is meant to operate around people on the factory floor. The eventual dexterous hand will likely also be safe by design: possibly with series elastic actuators (motors with spring elements) that naturally limit force and prevent sudden hard impacts. Apptronik has a background in exoskeletons and force-controlled robots, which suggests Apollo’s hands (when dexterous ones arrive) will have innate compliance to safely handle both fragile objects and interact with humans. Another feature is mass manufacturability – Apptronik touts Apollo as built for assembly line production. They even said Apollo is constructed from easily sourceable parts and designed with techniques like sheet metal fabrication and CNC machining that can be scaled. When they add complex hands, they will likely try to simplify the design to make it manufacturable too – perhaps using clever mechanisms to reduce part count. For example, they might use a single motor to drive multiple joints through a linkage, or use belts/pulleys to locate motors in the forearm (somewhat like Tesla’s tendon approach, but maybe more modular). Also, Apptronik has a partnership with NASA and others; they might incorporate space-tested tech like harmonic drive gears or tendon routing strategies proven in prior robots, giving Apollo’s eventual hand a robust pedigree. Culturally, Apptronik is taking a “walk before you run” strategy, which is unique among some startups that promise very futuristic capabilities out of the gate. By proving Apollo’s core value first, they build trust – and when the dexterous hand comes, it will be on an already reliable platform. In terms of software, Apptronik is looking to integrate Apollo with existing automation systems – so its hands will be programmed not in isolation but as part of a workflow (for instance, working with conveyor belts or autonomous mobile robots in a warehouse). This system-level thinking is unique; it means Apollo’s hand might have special adaptations to work with other tools (like a shape that’s perfect for picking totes, or quick-change sockets for attaching power tools). Indeed, Apollo may have swappable end-effectors as a feature – one moment a gripper, next moment a dexterous hand, depending on the task schedule. Overall, Apptronik’s uniqueness lies in its pragmatism and scalability: they innovate in process and integration more so than in raw tech, at least initially.

Fingers and Decision Rationale: The fully realized Apollo hand is expected to have five fingers, as that’s become the standard for a general-purpose humanoid. Apptronik knows from their NASA work that an opposable thumb and at least two or three other fingers are necessary for a wide range of tasks. If they target 16 DOF, they might do what other designs do: combine the last two fingers (ring and pinky) as a single unit or fewer DOF, to simplify. Often, giving the pinky one less degree of freedom is a common compromise – e.g., it might not fully oppose or have independent lateral motion. This saves a motor but retains most function. Apptronik might also use linkages so that one motor drives two joints in a finger (like bending the fingertip joint when the middle joint bends fully, akin to how many prosthetic hands work). Such decisions would keep part count lower. They likely considered a three-finger hand (like thumb, index, middle) which can do a surprising amount, but ultimately five fingers provide far better envelope of operation. Three fingers struggle with flat objects or complex tools. Given their goal of replacing human labor, five fingers make sense for maximum compatibility with human tools and environments. The initial 0-DOF gripper is just a stepping stone. It’s also possible Apollo might have multiple hand options: a two-finger heavy lift gripper for one scenario, and a five-finger dexterous hand for another. This flexibility could be a unique selling point: “Apollo can be whatever pair of hands you need.” So far, not many competitors offer that kind of modular hand approach; they tend to stick with one design per robot. Apptronik’s CEO has even mentioned working together with companies that are “cracking the dexterous manipulation problem” – implying Apptronik might collaborate or use others’ hand tech when it’s ready. So the Apollo platform could potentially adopt a third-party five-finger hand if it’s superior, which again shows how Apptronik values practical outcomes over doing everything in-house. This willingness to mix-and-match could yield Apollo units with state-of-the-art hands without Apptronik bearing all the R&D cost alone.

Sensors: On the sensor front, Apollo’s initial gripper probably has minimal sensing – maybe just limit switches to know when it’s closed or an approximate force sensor to avoid crushing items. The robot as a whole uses vision (cameras, depth sensors) to locate objects and guide the gripper. When Apollo upgrades to a dexterous hand, it will almost certainly include touch sensors and force feedback. Given Apptronik’s emphasis on safe interaction, each joint likely has torque sensing (either via current draw of motors or via strain gauges) to detect forces. The fingers could incorporate simple tactile pads on fingertips so Apollo knows when it’s touched an object and how hard it’s pressing. They might not go for ultra-high resolution tactile skin immediately (to keep costs down), but basic pressure sensors at key contact points (fingertips, maybe the palm) would be expected. In line with their industrial focus, they might integrate sensors that help with predictable handling – e.g., an internal sensor to detect if an object is slipping from the grasp (some grippers use accelerometers or acoustic sensors for this). Apollo’s arms and hands will likely feed sensor data into a control loop that ensures smooth exertion of force (this is something Apptronik has done in prior robotics – using feedback to modulate actuation for compliance). Regarding advanced sensors: temperature or chemical sensors are probably not in the hand – Apollo’s context doesn’t demand those. But one could imagine, if Apollo were to handle hot manufacturing parts, a thermal sensor on the hand might be added to avoid accidents. For now, focus is on force and position sensing to enable both strong and delicate actions.

Strength and Finesse: Apollo is built to be strong – it’s advertised as being able to lift heavy loads (one spec indicated it can handle a 25 kg payload, roughly 55 lbs). That’s on par with a strong human and very useful in warehouses. The hands, therefore, must at least support that load. The initial grippers presumably can hold 25+ kg objects firmly (using both hands together for balance). They might have locking mechanisms or power gearing to not slip under weight. Once dexterous hands are introduced, each finger won’t individually lift 25 kg, of course, but together the hand and arm should still manage similar payloads. The trick will be balancing strength with delicacy: Apptronik will design the hand so that it can grip a heavy box by the corners and also pick up a small screw. The latter requires precise control of small force – which they can achieve via their elastic actuators and control software. Apollo’s eventual five-finger hand is expected to handle delicate tasks like flipping a switch or pressing a keypad button as well, since those are common in industrial settings (machines with control panels). Their background with NASA’s Valkyrie (which had pretty dexterous hands with touch sensors and about 10+ DOF) suggests they know how to calibrate such actions. In terms of raw power, Apollo’s hand will likely not surpass Tesla’s or Sanctuary’s – those have more DOF and possibly more advanced actuation – but it will be sufficiently strong for most tasks. Crushing a can or cracking a nut would certainly be within its capabilities once the full hand is there. The design might incorporate high-strength materials at the fingers (like steel tendons or strong polymer links) to handle high forces without damage. On the fine end, using a pencil or unplugging a cable are target tasks when Apollo gets dexterous fingers – those require maybe a few Newtons of force and sub-centimeter accuracy, which are straightforward if they implement the expected sensors and control.

Notable Features: A notable aspect of Apollo’s development is its partnership with NASA and potential use in space or defense. While primarily aimed at Earth industries, Apollo’s robust and modular design could be appealing for future missions. If so, the hand might be designed to work in different environments (gloved or ungloved, perhaps). Another point is upgradability – customers could buy Apollo with simple hands now and retrofit better hands later, which is a different model from buying a whole new robot. This is akin to upgrading a tool head on a machine – Apptronik might offer the dexterous hand as an upgrade kit. It shows confidence that the base platform (arms, body) is capable enough to handle the added complexity later. Apollo also shines in payload-to-weight ratio: at 72.5 kg robot weight and 25 kg payload, it’s doing a significant fraction of its weight. That efficient design is partly because they didn’t overweight it with unnecessary components (like heavy hands not needed initially). The focus on “friendly interaction” means Apollo’s eventual hands may have soft coverings or padding to avoid sharp edges. They might 3D-print finger skins from rubber-like material to both improve grip friction and safety. This is a small detail but notable as a differentiator (some robots have bare metal fingers which can slip or cause damage if bumped; padded fingers solve that). Apptronik also emphasizes that Apollo is battery-powered and mobile – unlike some early humanoids that were tethered. So the hands are fully untethered too (no external hydraulics like Sanctuary’s early prototypes might have needed). Everything is self-contained, which is a feat of integration. It implies that any advanced hand they add must also be energy-efficient to keep battery life reasonable (Apollo’s battery is stated around 5 hours per charge). That probably means no crazy power-hungry actuators – instead they’ll use efficient motors and perhaps spring mechanisms to save energy (e.g., using spring return in fingers so that holding an object doesn’t constantly draw power). All these design decisions underscore Apptronik’s goal: a work-ready, versatile, and scalable robot. Apollo’s hands might not be the flashiest at first, but they are poised to become increasingly capable in step with demand, embodying a very engineering-driven, iterative approach to achieving human-like manipulation.

Figure AI (Figure 02): Balancing Strength, Dexterity, and AI Integration

Figure AI is a Silicon Valley startup (founded in 2022 by Brett Adcock, who previously founded Vettery and Archer Aviation) that burst onto the humanoid scene with significant funding and ambition. By 2025, Figure had unveiled its second-generation humanoid, Figure 02, after rapid development of an initial prototype (Figure 01). Figure’s emphasis is on combining advanced AI with robust hardware to create a general-purpose worker robot. A key highlight of Figure 02 is its advanced hands – human-sized, five-fingered hands with 16 degrees of freedom each and “human-equivalent strength.”. Figure’s team iterated through multiple hand designs quickly (they mentioned comparing second-gen and fourth-gen hands in under two years), indicating they are aggressively optimizing the hand’s performance.

Dexterity & Capabilities: With 16 DOF per hand, Figure 02’s hands are quite dexterous – each has nearly the full range of human finger motion, just slightly simplified. Typically, such a configuration might mean the thumb has 4 DOF (two at the base for universal movement, one for bending, one for the tip), and each of the four fingers has 3 DOF (knuckle, middle, and distal joints). That totals 4 + 4*3 = 16. So essentially, Figure has all five fingers articulated, just possibly lacking an extra degree here or there (for example, perhaps the pinky finger’s lateral motion might be constrained, or some coupling exists between joints). Despite any minor simplifications, the company claims the hands “rival human hand dexterity and fine manipulation”. In practice, this means Figure’s robot should be able to perform tasks like picking up tiny objects, using tools, typing, and other precise operations. They have shown confidence by partnering with a real-world manufacturing environment (BMW’s auto plant) to test the robot’s abilities in doing AI-driven tasks like data collection and presumably some light assembly or logistics work. The hands reportedly can achieve human-like grip strength – which suggests, for example, being able to firmly grip a 5+ kg object in one hand or apply the force needed to turn a stubborn knob. “Human-equivalent strength” likely means each hand can generate on the order of 50–100 N of grip force (which is what an average person can do). That’s enough to lift heavy tools, carry weighted objects, or exert torque on objects like jar lids. The dexterity combined with strength implies that Figure’s hands are aiming to be generalists, not specialized grippers. They can adapt to different shapes and tasks on the fly. This aligns with Figure’s goal of one day moving these robots from factories into the home – where hands must be versatile. The underlying technology is not fully public, but given the rapid development, they likely use electric drives (high torque motors possibly with gear reductions or tendon systems) and some clever mechanism design. They may have drawn inspiration from existing top-tier hands like the Shadow Robot hand (24 DOF) or others, choosing a slightly lower DOF count to reduce complexity while keeping key movements. The result is a well-rounded hand that balances dexterity (16 DOF is plenty for most tasks) with practicality (fewer motors than a 22-DOF hand, meaning less weight and simpler control).

Cost & Manufacturing: Figure has raised a huge war chest (over $100M initially and reportedly a Series B of $675M at a sky-high valuation), so they have the resources to invest in high-quality hardware. However, their strategy is also to scale production quickly, which means they care about manufacturability. The Figure 02 is said to be a “ground-up redesign” over the prototype, improving batteries, actuators, electronics – and the hands for better performance and presumably easier manufacturing. The 4th-generation hands mentioned suggest they went through several design iterations to optimize things like part count and assembly process. A 16-DOF hand with human-level strength is complex, but they might have, for instance, used a combination of custom and off-the-shelf actuators. They also emphasize integrated wiring and tight packaging– likely to make assembly easier and reduce failure points. This careful engineering will help control cost. Additionally, they have significant backing, which can subsidize early production costs until economies of scale kick in. One potential manufacturing edge: they could be using some automotive or drone industry techniques (since founder’s background includes aviation). For example, using high-strength lightweight materials like carbon fiber for finger structures or optimizing motor design for mass production. They might also partner with big contract manufacturers given their funding. Ultimately, they aim to produce at scale not just for industrial clients but perhaps even for consumers in the home “in the near future”. That implies a need to drive costs down dramatically. The hands, being one of the more complex sub-systems, might be a target for cost innovation – maybe through injection molded components for finger linkages, or using identical modular fingers to streamline production (some robotic hand designs use identical finger units for all four fingers to simplify manufacturing; the thumb can then be a modified version). If Figure can simplify parts and use economies of scale, they might achieve a moderate cost per hand. Right now, though, one can assume the prototypes are expensive (tens of thousands of dollars per hand, possibly), but that’s acceptable in small batch for a premium robot. Over time, they probably want to get the cost of the whole robot down to something competitive (maybe on the order of a car’s cost). They have not disclosed a price, but with their funding, they may not worry about initial sales revenue – focus is on proving capability and then scaling where cost per unit will drop.

Intended Usage: Figure’s near-term target is workforce tasks in industrial and commercial settings – similar to others, like warehouses, manufacturing, retail stocking, etc. They specifically tested at a BMW car factory, suggesting tasks like moving parts, fetching tools, or quality inspection might be in scope. The hands would be used to grasp car components or operate machinery. They likely plan for the robot to do things like attach parts, plug connectors, carry assemblies, and maybe even use wrenches or drivers. In logistics, Figure’s robot could pick and place items of various sizes (where a five-finger hand is beneficial because it can handle both small and large objects without needing different grippers). The mention of eventually expanding to the home means they envision these hands doing household chores as well – everything from doing dishes (manipulating plates, utensils) to folding laundry (requiring fine finger control), cooking (holding and using kitchen tools), or caring for the elderly (gentle touch required). It’s a broad scope, but the hand is built general enough for it. Initially, though, the focus is likely on more straightforward tasks such as box handling, equipment operation, and simple assembly tasks in controlled environments. They also highlight AI capabilities like an onboard vision-language model and speech conversation, meaning the robot is intended to interact with people and understand instructions. The hands in that context allow it to do whatever a person might verbally ask it to (within physical reason). For example, an instruction like “turn that valve, then press the green button” is possible because the hands can grasp and twist and also push buttons. In a store, it could restock shelves (picking up products and placing them properly). In an office, it might fetch items or operate light switches and doors. Essentially, Figure aims for multi-purpose deployment, so the hands are not specialized for one niche but rather a solid all-around tool – this is in line with the generalist vision akin to Tesla and Sanctuary.

Degrees of Freedom: 16 DOF per hand is a design choice that balances complexity and sufficiency. It covers thumb rotation, thumb flexion (3 joints possibly), and 3 joints on each finger, but possibly with one combined or passive DOF to trim from a full 20+ DOF count. Many research hands have 20+ DOF but control them with fewer actuators (underactuation). Figure might be doing something similar: maybe 16 active DOF and a few passive compliances. For instance, a common tactic is to have the last joint of the finger (the fingertip bend) be passive or mechanically linked to the middle joint – thus you count the DOF if free-moving, but you don’t actively control it with a separate motor. This yields simpler control but near equivalent functionality for many tasks. If that’s the case, the hand might effectively have, say, 16 motors controlling 20 joints, with 4 joints passively moving along. It’s speculation but plausible given typical designs. The difference between 16 and, say, Tesla’s 22 DOF might be smaller than it sounds if some of Tesla’s DOF are also passive or coupled (Tesla’s 22 DOF might include certain coupled movements too). Ultimately, 16 is enough to do independent movement of each finger and oppose them with the thumb in multiple ways. There might be no independent metacarpal abduction per finger except maybe index and thumb – often to simplify, some designers allow only the index finger to have side-to-side motion (for pointing) and couple the middle-ring-pinky base for a natural curvature. We’ll see. But clearly, Figure prioritized key human-like motions and trimmed out what they deemed less essential. They boasted these hands as “4th generation” implying they kept improving. Possibly early versions had less DOF or strength and they incrementally got to this spec. The DOF count is likely driven by the tasks they envision: anything requiring more DOF than that is extremely fine (like playing complex musical instruments or advanced sign language). For typical tasks, 16 DOF should suffice.

Unique Innovations: One unique facet of Figure is how strongly they integrate AI and autonomy with the hardware. For example, the mention of onboard vision-language models (VLM)suggests the robot can interpret visual scenes and language queries to figure out tasks, which is cutting edge. The reason this matters for the hand is that the AI could plan very human-like hand usage autonomously. For instance, if told “tidy up this room,” the AI can visually identify items and the hand is capable enough to pick up clothes, books, cups, etc. This holistic approach might allow more natural use of the hand rather than pre-scripted motions. Another innovation is their focus on power efficiency and integration – they specifically mention integrated wiring and better batteries, which, while not hand-specific, shows they are ensuring the hand’s many actuators are well managed power-wise. The “integrated cabling” likely means the hand doesn’t have messy external wires (important for reliability in production). There’s also a line about “3x the compute on board vs previous gen”– more computation can mean better real-time control of the hand’s many DOF, possibly even running learning algorithms on the edge to refine hand-eye coordination. And “Figure’s neural networks process images at 10 Hz, actions at 200 Hz” (from an external article snippet) suggests a high control loop frequency, beneficial for dexterous manipulation to respond to feedback quickly. On the mechanical side, since they achieved human-equivalent strength in a fairly small hand, they might have invented or optimized certain components. Perhaps custom high-torque density motors or gearboxes that are both strong and backdrivable (backdrivability = can be driven by external forces, helpful for safe and compliant interaction). They likely also incorporate feedback sensors in each joint to allow precise force/position control – these could be modern strain-wave gear encoders or magnetic encoders, etc. Notably, the founder often shares images comparing older vs newer hand designs, indicating they made rapid improvements – possibly using 3D printing for prototypes, then CNC for final. Innovation in how quickly they can iterate hardware is a bit of a secret sauce. In just over a year they went from concept to something trialed at BMW.

Another unique approach: collaboration with OpenAI was mentioned. If OpenAI is involved, maybe they will integrate cutting-edge AI (like GPT-based reasoning or advanced vision) to make the hand usage more intelligent than others. Imagine the robot learning new hand manipulations via AI much faster. So, while the physical hand is similar in spec to others, the smarts behind it might set it apart – the ability to generalize and learn tasks without explicit programming.

Fingers & Mechanical Details: The hands are human-scale, which is good for using existing tools. They mention “4th generation hands: the latest human-scale hands”, so presumably the size is akin to an average male hand (since robot is 1.7 m tall). They likely have hard fingertips (perhaps rubber-coated) for durability and grip. Possibly they have swappable fingertips – some designs allow changing the fingertip material or adding sensors. There’s also likely some compliance either built into joints or via control to gently grip irregular items. Having five fingers means it can do all major human grip types: power grip (fingers and thumb around an object), precision pinch (thumb to index), lateral pinch (thumb to side of index, like holding a key), tripod (thumb-index-middle for small objects), etc. With strong fingers, the robot can hold tools like screwdrivers or even a hammer. Actually hammering might be limited by whole-arm strength, but the grip is fine. They reported the robot can carry up to 25 kg and has six cameras for vision, so the hands could be used in heavy lifting tasks with vision guidance (like seeing and then two-handing a heavy box). Unique to Figure, from the PR: “six RGB cameras and advanced processing”in the vision, and improved audio sensors – meaning the robot could possibly locate things by hearing/touch too. But that’s peripheral.

Sensors and Feedback: There’s mention of “proprietary haptic tech mimicking touch” for Sanctuary and presumably Figure would also incorporate tactile sensing. While not explicitly stated, achieving human-level fine manipulation typically requires at least some tactile feedback. It’s possible that Figure 02 has integrated touch sensors in its fingers (maybe capacitive or piezoresistive pads at fingertips). At minimum, it will have force/torque sensing at the wrist – many robots include a 6-axis force-torque sensor there to feel what forces the hand encounters. That helps with tasks like insertion (feeling resistance if something is misaligned). If they haven’t added fingertip sensors yet, it could be on the roadmap, as they iterate fast. The six cameras could include some in the hands (though more likely they’re all head/body cameras for navigation and object recognition). But interestingly, some designs put small cameras in the palm or fingers to get close-up views for manipulation. Not sure if Figure does that yet. However, the detail that the neural nets process images at 10 Hz and generate actions at 200 Hz suggests a high sensitivity control loop. Likely the 200 Hz loop uses internal sensors (joint encoders, possibly accelerometers) for smooth motion and quick reaction (like if a slip is detected via a subtle sensor cue, it can adjust grip). The 10 Hz vision loop might identify objects and positions. If they integrated something like torque sensors in the finger joints (so the robot knows how much force each joint is exerting), it could deduce when it’s gripping an object firmly or if it’s touching something unexpectedly. Those would be part of internal sensors. Given their tech-forward stance, they might also experiment with fancier “electronic skin” in future iterations. But currently, 16 DOF with “human-equivalent strength” implies a focus on the mechanical side; any mention of advanced sensors might come in later updates. On the UI side, they did mention things like improved microphones for voice and presumably the ability for the robot to respond verbally or via gestures – the hands might even be used to gesture or point naturally thanks to their dexterity. This could make interaction with humans more intuitive (pointing at an object it’s referring to, giving a thumbs-up, etc.). Small note: They improved “microphones and sensors” in the hands for sensory equipmentwhich suggests the hands indeed are considered part of the sensory apparatus. Possibly meaning they added sensors to the hands by the Figure 02 model (the snippet mentions improved sensors in the context of “hands: 16 DOF; sensors: improved microphones and sensors”– slightly unclear but could imply the hands include sensors as part of overall improvements). If so, that confirms tactile or force sensors got an upgrade from the first gen.

Strength and Fine Control: As said, “human-equivalent strength” means it can do both gentle and forceful tasks. They specifically brag that the hands enable a wide range of human-like tasks– which presumably includes heavy lifting and delicate manipulation. Achieving human strength also means the robot can hold something like a power drill and actually apply it (drilling often needs pushing force and control to not strip screws). Or turning a valve that’s tight (humans can exert significant torque with a firm grip). Many older robot hands were too weak for such tasks or would break if forced; Figure’s aim was to remove that limitation. At the same time, fine motor should allow tasks like unplugging a USB cable or picking up a coin, which they likely test. If an object is very light or fragile, the AI or control can command the hand to use minimal force – encoders and torque sensors allow them to gauge that. Testing at BMW suggests they might have tried tasks like assembling some interior components or moving delicate auto parts, which would confirm fine control. As these robots progress, an interesting metric will be speed – can the robot’s hands move quickly enough for certain tasks? Possibly yes, since no mention of slowness; they likely can open/close in a second or so, and reposition fingers quickly. That’s important if doing repetitive tasks to keep up with humans or conveyors.

Notable Features: One notable thing is that Figure’s timeline from founding to advanced prototype is extremely short – they seem to employ a lot of top talent and move fast, which means they might incorporate the very latest technologies (like new AI algorithms or new actuator tech). They even have a partnership with the U.S. Air Force and potential defense use (one of their publicized partnerships is with the U.S. military for evaluating humanoids). That could push them to ensure the hands are robust and capable for things like handling military equipment or performing maintenance tasks on Air Force bases. In such contexts, reliability is as important as dexterity, so Figure likely invests in durability testing of the hands – temperature extremes, dust, etc. Another interesting note: their funding and talk of building “world’s largest humanoid pretraining dataset”suggests they might simulate and train the hand in virtual environments at scale. This could result in the hand being smarter in how it handles objects compared to others that might rely more on classical programming. For instance, the robot could learn through AI to adjust its grip if an object starts slipping without explicit programming – a learned reflex, essentially. Over time, that could give Figure’s robots a performance edge. Also, their mention of “collaboration with OpenAI” means they might integrate GPT-like reasoning, which in turn could allow it to improvise new uses for the hand (like figuring out how to use an unfamiliar tool by itself). Physically, something notable is that Figure’s hand has been iterated multiple times in a short period – possibly they have tested different numbers of fingers, different motor placements (in-hand vs forearm), etc., and settled on something. Perhaps originally a simpler gripper, then a 3-finger, then realized 5-finger needed, etc. The final design presumably chosen because it gives the best versatility. This iterative approach parallels agile software development, which is somewhat new in robotics hardware.

In summary, Figure’s hands encapsulate a modern, AI-driven approach: nearly as dexterous as the best, strong enough for real work, integrated into a platform that can reason about how to use them. While not radically different in spec from say Tesla’s eventual hand (except a bit fewer DOF), the combination of design decisions and AI integration could make them extremely effective in practice – which is ultimately what matters for a robot that’s supposed to be out there doing jobs.

1X Technologies (Neo): Safe, Soft-Touch Hands Aimed at Home Robotics

1X Technologies (formerly known as Halodi Robotics) is a company that, interestingly, has been developing humanoid and humanoid-esque robots with an emphasis on safety and affordability, particularly for use in everyday environments (like homes and offices). One of their robots, Neo, is a humanoid intended for home use as a sort of general assistant. 1X is backed by investors like OpenAI, and they have been working on humanoids since 2014, including earlier models like Eve (a humanoid on wheels). Neo is their bipedal model, unveiled around 2023–2024. Neo’s hands are designed to be capable yet inherently safe – prioritizing compliant, gentle interaction – since a home robot must be very trustworthy around people. The Neo hand is a five-fingered design, roughly human-sized, but achieved with a clever low actuator count: it reportedly has 21 degrees of freedom in total, driven by only 11 actuators via a system of linkages in the fingers. This means Neo’s hand is underactuated – it has fewer motors than joints, using mechanical couplings to still realize complex motions.

Dexterity & Technology: With 21 DOF, Neo’s hands actually approach human-level kinematic complexity, but the use of only 11 actuators suggests a lot of the movements are coupled or passive. For instance, each finger might have multiple joints that move together when one actuator pulls a tendon. 1X’s design philosophy often involves tendons and springs – similar to how our own fingers are moved by tendons from forearm muscles. Indeed, 1X has shown that tendon-driven motion is at the core of Neo’s limbs for smooth, “soft” action. The advantage here is that the hand naturally has some give and compliance, making it safer (if the hand bumps into something, the tendons can flex a bit, not causing hard damage). The 11 actuators might be arranged such that: the thumb has a couple motors (for different axes), a few motors control groups of the other fingers, and maybe one motor controls finger abduction (spreading). For example, the index and middle finger might each get an independent control, while ring and pinky share one, etc. Despite these couplings, 21 degrees of freedom mean the hand can still wrap around objects in a form-fitting way, even if not every joint is independently controlled. The dexterity is likely sufficient for daily tasks: Neo’s hand can grasp various shapes (cylinders, spheres, flat objects) and perform basic manipulations. It might not be as dexterous as, say, Sanctuary’s or Tesla’s on an individual finger basis, but it is much more dexterous than a simple two-finger gripper. One can envision Neo’s hand picking up a glass, turning a doorknob, or picking a pen off a table. The fundamental technology – tendon-driven with linkages – also results in a very low mechanical impedance (i.e., it’s easy to backdrive the hand). This means if a human grabs Neo’s hand or a finger, it yields easily rather than resisting. That’s a deliberate safety feature and also means the hand can do delicate tasks with low force. 1X has demonstrated their robots doing things like tidying a room or using keys, which indicates the hands are indeed functional for those tasks. The underactuation does impose some limits: for extremely fine tasks like tying knots or fast typing, the lack of independent control for each joint could be a barrier. But 1X likely judged that for home tasks (cleaning, fetching, operating appliances), full independent control is overkill.

Cost & Manufacturing: 1X is very cost-conscious. As Halodi, they built robots with off-the-shelf components (they famously used hoverboard motors in their wheeled robots to save cost). The Neo hand’s design with 11 actuators instead of, say, 20, is partly to reduce cost and complexity. Fewer motors = fewer expensive parts and simpler control electronics. It also means fewer points of failure. By using tendon linkages, they can place motors perhaps in the forearm (to reduce weight in the hand) and use inexpensive high-torque motors to drive multiple joints. Also, the hand likely uses a lot of plastic and composite parts rather than all metal. 1X’s ethos includes using mass-manufacturable parts – they might injection mold finger segments or use 3D printed high-strength polymers to lower cost. They also integrated the hand with a soft fabric “knit” covering over the whole robot (including perhaps gloves for the hands), which is not just for appearance but also to reduce wear and tear and allow using cheaper underlying materials because they don’t need a perfect cosmetic finish. Cost advantage might also come from the scale: 1X envisions deploying potentially many home robots, so they aim to make the hand as simple as possible per unit. The decision to go with 4 fingers + thumb but lower DOF per finger could drastically simplify manufacturing. For example, one motor with a clever cam or differential could drive the closing of two fingers at once. This is reminiscent of some commercial robot hands like SoftHand or RightHand Robotics’ grippers which achieve versatile grasps with fewer actuators. The result is robust and cheaper. By focusing on strictly what’s needed for the home, they avoid pricey excess. Another cost saver is that their actuators might not be exotic – they often use direct-drive or quasi-direct-drive motors with low gearing to achieve quiet, smooth motion, instead of expensive gearboxes. This can reduce part cost (gears, custom gearboxes can be pricey, whereas a standard BLDC motor is cheap in quantity).

Intended Use Cases: Neo and its hand are meant for domestic and light-duty tasks. Think of chores like tidying up clutter, loading/unloading a dishwasher, bringing you objects, or even simple food prep. The hand is designed to handle everyday objects found at home: cups, utensils, clothing, electronics, knobs, handles. It has five fingers so it can grasp irregular things like toys or pillows, and operate typical home fixtures (doors, faucets). 1X has shown interest in the robot performing security patrols or elder care assistance in home or office – for that, the hand needs to perhaps open doors, pick up phones, or press emergency buttons. Another angle: they partnered with a security company (ADT) to trial a humanoid for night security – the robot might use its hands to open doors or pick up objects that are out of place. In the home companionship role, the hands should be gentle enough to interact with people (e.g., handing something to a person or even providing support to someone standing up). The high compliance of the hand suits that – it won’t accidentally pinch too hard. Unlike industrial robots, 1X’s home robot doesn’t need to lift super heavy loads. It likely focuses on a range of a few kilograms at most (maybe carrying a bag of groceries). The strength is probably moderate (maybe each hand can lift 2–5 kg comfortably). But that’s fine for domestic tasks. They specifically listed tasks like tidying, cleaning, home management, conversation, tutoring. Tidying could mean picking up items from floor or table and putting them away – the hand must handle items from small (toys, books) to medium (blanket, dishes). Cleaning might involve holding cleaning tools (a rag, a spray bottle – which Neo’s hand can manage with a basic grasp). Home management might mean manipulating appliances (the hand turning knobs on a stove, pressing microwave buttons, etc.). So the design emphasis is on versatility and human-oriented motions, not on heavy or super-fine specialized tasks. In contrast to, say, Tesla aiming first at factories, 1X aims at living spaces, which demand a very friendly, safe hand that can do a bit of everything at a slower pace.

Degrees of Freedom: We have the number 21 DOF for the hand, presumably including some wrist DOF (though likely they count wrist separately). But the Reddit snippet clearly said “hand has 21 DOF” controlled by 11 actuators. That indicates significant underactuation. Underactuated hands often have more passive DOF – for example, each finger might have 3 joints (that’s 15 for five fingers) and the rest DOF are things like thumb rotation, finger spread, etc., summing to ~21, but actuators control only some of these. A guess: 1X might have done something like: one actuator controls the spread of the four fingers (abduction/adduction together), one for thumb rotation, one for thumb flex (or two for thumb if needed), then maybe one for index finger flex, one for middle finger flex, and one shared for ring+pinky flex (that’s an approach Shadow Hand can do, linking ring and pinky), plus possibly some coupling in the fingers themselves (like when an actuator closes a finger, multiple joints bend through mechanical linkage – e.g., as in the RobotIQ gripper or similar adaptive hands). This could come out to ~10 or 11 actuators. The result is a hand where the fingers naturally wrap around objects when closing, thanks to passive adaptability (springs allow each joint to give as needed). In practice, this means Neo’s hand can conform to an object shape without each joint being individually commanded – a very human-like property since our own tendons couple joints too to some degree. The downside is you can’t pose each joint arbitrarily (like you couldn’t make a “Vulcan salute” with split fingers if actuators are coupled). But again, for everyday tasks, that’s unnecessary. 1X decided those extra DOF weren’t needed enough to justify extra actuators. By contrast, their total body DOF is around 20 for arms and legs plus head, so the hand with 21 DOF is nearly half of the robot’s DOF but they only devote 11 motors to it, which is a smart resource allocation.

Unique Features: One standout feature is how quiet and gentle 1X’s robots are designed to be. Halodi’s earlier robots used very quiet actuators (people often note you can barely hear them move). This is crucial for a home robot – you don’t want a loud whirring machine. The tendon and compliance in the hand likely keep it quiet (no high-speed gear whining). The “knit suit” soft covering on Neois another unique element: it covers the arms, torso, and possibly the hands. This soft exterior does a few things: protects humans from any hard edges, gives the robot a more approachable look (less like cold metal), and it can hide wiring and allow for stretching with motion. For the hands, maybe they wear a glove-like layer that provides grip friction and also a bit of cushioning. 1X’s focus on safety also led them to implement features like low inertia – if the hand swings or falls, it won’t hit with much force. The limited actuators and inherent compliance mean the hand is intrinsically safer than a stiff, fully actuated one. Another unique design choice: 5-linkage fingers as mentioned– possibly meaning five bar linkages or complex finger mechanisms – could allow unusual motions or ensure that, for example, the fingertip stays oriented in a useful way as it closes (some finger mechanisms keep the pad oriented towards the object). Also, the 1X team historically looked at human ergonomics, so Neo’s hand might mimic not just degrees of freedom but also the strength distribution of a human hand – meaning maybe the index and middle are strongest for fine pinch, ring and pinky contribute in power grip but not prioritized for precision. They might have calibrated the tendon tensions to reflect that (so that in a normal grasp, index/middle take more load, etc.). Moreover, being backed by OpenAI, there’s a notion they might eventually incorporate advanced AI to control the hand, similar to others. But near-term, I suspect 1X’s control is more heuristic and teleoperation-driven (they have shown teleoperating their Eve robot with VR, which had simpler hands/clamps). They might gather data by teleoperating Neo in a home-like environment to teach it how to manipulate things, and the hand’s compliance will help a human operator not break stuff as they learn.

Fingers & Number of Fingers: 1X explicitly went with five fingers (“hands are the key to the world around us. NEO’s hands are built to handle important jobs around your home.”). They didn’t try something like removing the pinky or having only three fingers. This shows they value familiarity and capability – five fingers ensures the robot can grasp basically anything a person can, in a similar manner. This is important in a home where objects are not standardized. If the pinky were absent, tasks like holding a wide tray or stabilizing a jar might suffer. Also, for personal interaction, a five-finger hand can do things like shake a human’s hand properly or give a high-five – not trivial, but it does make the robot more anthropomorphic and socially acceptable. The decision to have fewer actuators but keep the actual finger count is clever: you still get the benefit of five contact points, which improves grasp stability and adaptability, without needing to independently control every one. For example, if Neo wants to pick up a coin from a table, it could use an underactuated pinch where the index and thumb do the main work while other fingers maybe automatically curl out of the way. It can perform that because it physically has the fingers – even if not all joints are motorized, the passive ones will yield as needed. In contrast, a robot with only two fingers physically would struggle to pick up a flat coin. So finger count is crucial for versatility.

Sensors and Touch: 1X likely uses fairly simple sensing in the hand to keep costs down. They definitely have position encoders for each actuator (so they know how far a finger has moved). They probably also measure motor current or include some series elastic element to infer force – i.e., an actuator driving a tendon with a spring can measure spring deflection to know force. In an underactuated hand, controlling grip force is tricky because one motor might drive multiple joints, but one method is to monitor motor torque and once an object is contacted (torque rises), then you maintain a certain torque to hold it. Given the focus on not damaging things, they likely incorporate torque limits. They might not have high-resolution fingertip sensors at this stage – I haven’t seen mention of tactiles. However, they might integrate some basic contact sensors. Possibly the glove material itself could have capacitive or pressure sensors. Even if not, the compliance and control might suffice for tasks (e.g., if the hand closes and doesn’t detect much torque increase, it knows it hasn’t grasped anything, so adjust approach). Also, vision can compensate: 1X’s Neo uses camera vision to guide hand placement, so it might visually determine when an object is in hand or if something was dropped. Interestingly, because they built it for home, they might have included something like finger pinch sensors for safety – some robots have small sensors to stop the hand if it’s about to pinch a finger with something (to not hurt a person’s finger accidentally). This is speculative, but home use demands thinking of weird accidents. The knit suit might also cover potential pinch points. For feedback, the robot likely relies heavily on its actuators’ inherent backdrivability: if a finger bumps an obstacle, the motor feels the resistance (via current spike or via the springs) and then can stop.

Strength and Softness: We touched on this – Neo’s hands are not the strongest in this lineup. They probably aim to handle a few kilograms – for example, could Neo open a tightly sealed jar? Possibly not as easily as a human, since underactuated hands sometimes struggle with tasks requiring high independent forces (like unscrewing a jar requires strong thumb-index force and counter-force from the other fingers). If it’s not strong enough, maybe in such cases the approach would be to bring the jar to a fixed surface and use two hands differently. But typical tasks like carrying laundry or pushing a vacuum or pulling a door handle – those are within likely strength. One report said Neo’s grip strength is strong enough to deadlift 70 kg (which likely refers to whole body deadlift, not hand – might be misinterpreted in a Medium article). It said “grip strong enough to deadlift 70kg” – that might mean combined with the arms, it can support that? This seems high, and possibly mis-stated. I suspect that’s more about whole robot capacity. But each hand might lift, say, 5-10 kg if needed for short times (like picking a small chair). The key is, the control will ensure it doesn’t apply such force in a dangerous way. On the delicate side, Neo’s hand can certainly pick up something as light as a paper or a potato chip. The compliance inherently means it won’t crush things by accident – if the motors only provide moderate torque and are compliant, the worst case might be dropping something, not breaking it. They likely tuned the maximum force to human-like levels or lower. Since it’s meant to interact with humans physically (maybe helping a person stand or handing them objects), they needed to guarantee the hand doesn’t exert excessive force.

Notable Features: One very notable thing with 1X’s Neo is the emphasis on companionship and collaboration. They even mention abilities like “conversation” and “tutoring”– implying an almost social robot aspect. The hands could potentially be used in expressive ways (gentle gestures, waving). The safe tendon system ensures the robot could, in theory, even physically interact (a handshake or guiding someone’s hand) without causing harm. That’s a frontier others haven’t pushed much because they target non-human-facing tasks first. Also, 1X’s approach to open development is interesting – they show glimpses on social media, like the first pictures of the hand and mention specs on Reddit. It’s a bit community-engaged. They even open-sourced some stuff. That could lead to external input making the hand better. For instance, a robotics hobbyist might develop a new control algorithm for Neo’s underactuated hand.

1X also may lean on telepresence: earlier Halodi robots were teleoperated for security tasks (a human remote pilot sees through robot’s cameras and uses VR controllers to move it). Neo’s hand, being underactuated, might actually be easier for a human teleoperator to control (fewer degrees to worry about individually). The operator can just command a grasp and the mechanical design ensures it wraps the object. This synergy between hardware simplicity and teleoperation is clever – it offloads complexity to the physical mechanism.

In conclusion, 1X’s Neo hand is all about pragmatic design for living with humans: enough dexterity to do most household tasks, strongly geared towards safety and softness, and built in a cost-conscious way that could eventually make home robots economically feasible. It’s not chasing the last ounce of performance (like solving Rubik’s cubes blindfolded or something), but rather the right mix to be useful and trustworthy in our everyday spaces.

Other Notable Entrants: China’s Quest for the Ultimate Robot Hand

While American startups and Tesla get a lot of attention, Chinese companies are also racing to develop humanoid robots – and especially dexterous hands. In China, this effort is sometimes described as conquering the “final frontier” of humanoid development. A few notable Chinese projects include Linker Robotics and PaXini Tech, which are pushing hand technology to remarkable levels, as well as tech giants like Xiaomi and Fourier Intelligence exploring humanoids.

Linker Robotics (Linker Hand): Linker Robotics recently unveiled what they call the “Linker Hand” – a robotic hand aimed at being the most dexterous on the market. In a research version, the Linker Hand boasts an astounding 42 degrees of freedom, surpassing even the human hand’s typical 26–27 DOF and outclassing the well-known UK-built Shadow Hand (with 26 DOF). Essentially, Linker has added more joints than a human hand has, theoretically giving each finger up to 7 DOF (humans have about 4 DOF per finger if you count all movements). This implies they might be adding extra articulation, perhaps a double-jointed thumb base or additional side-to-side motions in each finger segment. The Linker Hand is also packed with sensors: it’s said to have an “advanced multi-sensor system, including cameras and electronic skin”. “Electronic skin” suggests a layer of tactile sensors across the hand’s surface, capable of detecting pressure, texture, maybe even temperature – mimicking the human skin’s sense of touch. Tiny cameras possibly in the fingertips or palm could help with precise positioning or inspecting objects in-hand. The goal of all this is to push dexterity to superhuman levels; indeed Linker claims their hand’s dexterity is the highest in the world. With 42 DOF, it’s essentially a research platform – likely expensive and complex – but it could achieve feats like manipulating a Rubik’s cube or doing intricate finger motions beyond any other robot. They even envision deploying up to a million humanoid robots with such hands in the future to collect data by doing fine motor tasks (they cite things like playing with Rubik’s cubes or even putting on makeup as test scenarios). In terms of cost and uniqueness, if they truly commercialize a 42-DOF hand, it would be a marvel but possibly impractical for widespread use until cost falls. It’s likely currently a prototype to showcase what’s possible. The sensors (electronic skin) set it apart – few others have integrated that to this degree. That could enable very subtle control, like adjusting grip based on slight slip or feeling the difference between materials. In short, Linker is pushing the envelope in raw capability, although such a hand might be overkill for many tasks. They plan to demonstrate its capabilities in the coming years, showing off things like finely handling objects (the mention of playing with a Rubik’s Cube and even putting on makeup implies a desire to reach an incredibly human-like (or superhuman) finesse).

PaXini Tech (DexH13 Hand): PaXini is another Chinese company focusing on haptic tech and humanoid robotics. They have developed a dexterous hand called DexH13 Gen2. Notably, this hand has four fingers rather than five – it’s a “four-finger bionic hand”. Typically, a four-finger design might omit the pinky finger, having a thumb and three fingers (index, middle, ring) or something akin to that. PaXini emphasizes that DexH13 integrates multi-dimensional tactile sensing + AI visual sensing in a dual-modal system. That means the hand is equipped with tactile sensors (likely pressure sensors on fingertips and possibly along the fingers) and also works in tandem with computer vision (cameras) – the combination allows it to “perfectly simulate various complex movements of human hands such as welding, grasping, rotating, and pinching”. Welding is an interesting example: it implies very steady manipulation and perhaps feeling the pressure on a welding torch or seam, tasks that require both precision and some force. Grasping and rotating are standard, pinching indicates fine control. So despite having only four fingers, the DexH13 Gen2 is presented as extremely capable in executing human-like hand motions. The rationale for four fingers could be to simplify control and mechanics (fewer fingers to actuate) while still covering most functionality. Many tasks can indeed be done with thumb + three fingers (some roboticists note the pinky’s contribution is mostly in enhancing grip strength and not absolutely necessary for many manipulations). If PaXini found that sacrificing the pinky allowed them to pack more sensors or stronger actuators into the other four fingers, that might be a trade-off they chose. This design could also be slightly more compact or robust (fewer moving parts). PaXini’s focus on haptics (touch) is evident: multi-dimensional tactile means they likely measure not just normal force but maybe shear forces or vibrations at the fingertips, giving the hand a rich sense of touch. This would enable delicate operations like handling fragile items or discerning object properties by feel, and also dynamic tasks like feeling when a nut is tight while turning a bolt. AI visual indicates they use camera feedback to complement touch – probably the AI helps decide how to grasp an object from vision, then touch sensors fine-tune the grip. PaXini claims these features let their hand simulate human hand movements perfectly, which is a strong claim. If true, tasks like threading a needle, typing on a keyboard, or using a tool could be within its domain. PaXini is essentially pushing the integration of sensing and AI as much as the mechanical DOF. So it’s unique in being a very sensing-rich hand. One can imagine it being used in scenarios requiring extreme precision plus awareness, like microsurgery (in the future) or delicate assembly of electronics.

Xiaomi (CyberOne humanoid): Xiaomi, better known as a smartphone and electronics giant, surprised the world by unveiling a full-size humanoid called CyberOne in 2022. CyberOne stands 177 cm tall and has a total of 21 DOF in its body. However, Xiaomi did not prioritize an ultra-dexterous hand for their prototype. In fact, CyberOne’s hands are more like simple grippers. The breakdown of its 21 DOF shows that the “gripper: 1 DOF x 2”– meaning each hand has just 1 degree of freedom, basically an open/close motion. So, CyberOne’s hands are basic clamps, capable of holding up to about 1.5 kg weight per hand, but not capable of fine finger movements. This likely was a pragmatic decision for Xiaomi’s initial foray – focus on walking and balancing, use simple hands to at least pick things up. The grippers are presumably like claw hands that can grasp medium-sized objects (they showed it handing a flower to someone during the demo, for example). In terms of dexterity compared to others, CyberOne’s hand is far behind – essentially equivalent to a basic two-fingered robotic gripper. It can’t do human-like manipulations beyond grabbing and releasing. Xiaomi’s roots are in mass electronics, so if they continue, one could expect them to try to improve cost-effectiveness (maybe eventually giving CyberOne better hands through their supply chain). But at the moment, CyberOne is more a demonstration. It shows that not all entrants immediately go for dexterous hands – some opt for simplicity to get a humanoid out the door. If CyberOne evolves, perhaps Xiaomi will incorporate more DOF (they certainly have resources to adopt others’ approaches or develop their own). But by end of 2026, unless they announce an upgrade, CyberOne probably remains a limited-dexterity system. This highlights an interesting contrast: some companies (like Tesla, Sanctuary) aim for full human-like hands from the start, others (Xiaomi, and to an extent Apptronik’s initial approach) start simpler.

Fourier Intelligence (GR-1 humanoid): Fourier is a Shanghai-based company known for rehabilitation robotics that entered the humanoid arena with their GR-1 humanoid. GR-1 is a roughly human-sized robot focusing on roles like physical therapy assistance or carrying objects. Its hands are moderately capable – Fourier stated the GR-1’s hands have 11 degrees of freedom in total. This likely means each hand has a few DOF (maybe 5–6 per hand, if 11 is total for both? Or 11 each, but context suggests maybe 11 in total for both). If it’s 11 per hand, that’s decent (like a simplified five-finger hand). If 11 total, then maybe each hand has ~5 DOF, which could mean a thumb and two-finger setup or some coupling. They mentioned it allows the robot to securely grasp items– so presumably a stable power grip for things like bottles, tools, etc. The focus of GR-1 is somewhat on strength (it can carry a significant portion of its weight) and safe human interaction. So the hands are probably not extremely dexterous, but enough for basic tasks – akin perhaps to an advanced gripper with maybe a thumb + two fingers that have a couple joints each. They might not target intricate finger gaiting tasks, but more about being a “helper” to lift or hold things (like helping a patient stand by giving them a hand, or carrying a tray). As a company with rehab background, they may have considered tactile sensing, but specifics are scant. It’s more of a middle-ground approach: more than Xiaomi’s claw, less than a full multi-fingered system like Linker’s.

Overall, Chinese efforts in robot hands show two directions: (1) High-tech, high-dexterity prototypes (Linkerbot, PaXini) which are pushing boundaries in DOF and sensing – arguably even beyond what Western companies have shown publicly – and (2) Pragmatic hands for near-term use (Xiaomi, Fourier) which stick to simpler designs for now to ensure reliability and quicker deployment. By 2026, it’s quite possible that some of these Chinese dexterous hand prototypes will start appearing in real humanoid robots. For example, Linkerbot aims to eventually equip possibly a million robots with their hands– an ambitious goal that implies working on cost and production scaling. If they succeed, the world could see very nimble-fingered Chinese humanoids working in areas like electronics assembly or service roles, collecting huge datasets of manipulation. It’s also a sign that the global race in humanoid robotics isn’t just about legs and AI, but very much about who can master the hand.

One more note: Unitree Robotics (a Chinese quadruped maker, now also making a humanoid called H1) should be mentioned. Unitree recently introduced the Dexterous Hand “Dex5”, intended for their upcoming humanoid. The Unitree Dex5 hand has 5 fingers and 20 DOF (16 active + 4 passive), and it’s loaded with 94 tactile sensors per hand. It’s essentially a competitor to high-end Western hands but from a company known for low-cost hardware. Dex5 has micro-force controlled joints, 1 mm precision, and can grasp up to 4.5 kg with a 10N fingertip force. This shows Chinese companies are adopting the best of both worlds: sophisticated design (similar to Sanctuary or Shadow hand) and potential cost efficiency. Unitree specifically, known for selling robot dogs at a fraction of Boston Dynamics’ price, might do the same in humanoid hands – democratizing them. If by 2026 Unitree’s humanoid H1 with Dex5 is out, that’s definitely a top entrant: 5-finger, 20 DOF, tactile-rich hand at (likely) lower cost than Western equivalents. It has backdrivable joints and high-speed reflexes, meaning it’s very agile and safe. So, Unitree could bridge the gap: a very advanced hand that’s actually commercially offered relatively soon (they even listed it on RobotShop as coming soon). We did mention Dex5 indirectly earlier, but to sum up: Unitree Dex5 is unique in combining advanced tech (94 sensors, etc.) with the Chinese cost-focused ethos, and it’s specifically designed for their H1 humanoid which they plan to mass-produce. Unitree’s approach underscores that competition will drive all players to up their hand game.

In conclusion, the landscape of robotic hands by end of 2026 will likely feature ultra-dexterous hands from both West and East. Chinese innovations are ensuring that features like electronic skin, camera-in-hand, and extreme DOF counts are explored. Some will find their way into real products (maybe in slightly toned-down form for cost). Others will remain bleeding-edge experiments. But the takeaway is: robotic hands are evolving rapidly worldwide, and the competition is yielding better, more capable designs at (hopefully) lower costs.

Robotic Hands vs. Human Hands: How Do They Measure Up?

After surveying these cutting-edge robot hands, it’s clear that humanoid robotics has made tremendous progress – yet the human hand remains a high bar. The human hand is often cited as nature’s most versatile tool: it packs over two dozen degrees of freedom, controlled by 40 muscles (20 intrinsic to the hand and 20 in the forearm), and loaded with thousands of sensory nerve endings for touch, temperature, and pain. It can exert significant force – a human grip can easily crush a soda can – and at the same time handle a fragile egg or contact lens gently. How do the latest robot hands compare on these fronts?

Degrees of Freedom: Top robot hands are closing in on human-level joint complexity. Tesla’s newest hand has 22 DOF (human ~27 DOF if counting subtle movements), Sanctuary’s about 20–21, Linkerbot even exceeded human at 42 (research). Many others in the 16–20 range approach the kinematics of a human hand. This means robots can now articulate fingers in ways nearly as nuanced as we can – thumbs that rotate and oppose, fingers that bend at multiple joints, some even capable of lateral finger spread (abduction) for a wider grasp or typing action. However, having joints is one thing; controlling them coordinately is another. Humans have a lifetime of neural control and sensory feedback that allows fluid, synergistic motion. Robots, even with equivalent joints, often use simplified control schemes initially – for instance, some underactuate or couple joints, whereas in a human every joint can be subtly controlled by the nervous system in concert. As of 2025, Elon Musk remarked that replicating the human hand’s 27+ DOF along with its fine muscle control accounts for “80% of the difficulty” in humanoid robotics. So while DOF counts are now comparable, functional dexterity still often lags because the coordination and strength distribution in robots are not yet as refined as nature’s design. But the gap is closing: robots can perform increasingly delicate manipulation tasks that require many joints – like picking up tiny objects or adjusting an item in-hand – which a decade ago were far out of reach.

Strength: Many of the new robot hands have human-like strength in at least some motions. Figure’s hand is explicitly “human-equivalent” in strength, meaning it can probably grip with tens of Newtons – enough to lift heavy objects and perform tasks like turning stiff knobs or using tools. Sanctuary’s hydraulic hands likely even exceed human hand strength in certain aspects (hydraulics can provide a very strong grip – possibly well beyond the ~100N of a strong human squeeze). Tesla hasn’t published grip force, but given Optimus can deadlift 73 kg with the whole body, the hands should be able to handle at least moderate loads without slipping. Humans, of course, have a remarkable range: from power-lifting heavy weights (our hands can support our entire body weight – think pull-ups or carrying groceries) to feather-light touch (holding a baby chick). Robots are nearing the heavy end – some can lift 20–30+ kg objects– essentially matching or exceeding an average person’s easy lifting range. One even sees claims like 1X’s Neo being able to “deadlift 70 kg” (though that likely involves full body usage, it indicates strong hands). On the fine end, robots are surprisingly good now: they can hold an egg without cracking it, or catch a raw egg (some demos have shown that). Modern control and compliance allows delicate touch. For example, Digit’s video showed handling an egg crate gently, and Optimus delicately manipulating small components. But one area humans still beat robots is dynamic strength control – e.g., we can reflexively modulate grip if something starts to slip, or instantly tighten if something is heavier than expected. Robots are learning this (with tactile sensors and fast control loops, some are demonstrating reactive grip adjustments), but the human nervous system (and millions of years of evolution using hands) still gives us an edge in swiftly adjusting force. Also, endurance and resilience come into play: a human hand can get a bit tired, but it also self-repairs small injuries, the skin regenerates, cuts heal. Robot hands under heavy use suffer wear – cables stretch or fray, motors can overheat, gears wear down. They don’t “heal” on their own. Engineers mitigate this with robust materials and testing (Sanctuary boasting 2 billion cycles on valvesis about ensuring longevity). But at some point, maintenance is needed. So in sheer strength, top robot hands are now in the human ballpark or beyond, but maintaining that performance reliably under varied conditions is a challenge where biology still shines.

Sensory Feedback: The human hand is rich with mechanoreceptors (for pressure, vibration), thermoreceptors (for temperature), and nociceptors (for pain). We can feel a faint breeze on our skin or distinguish a smooth surface from a rough one by touch. Robot hands are starting to incorporate tactile sensing but are not yet as comprehensive or high-resolution. A few high-end hands have multi-point tactile arrays (e.g., 94 sensors in Unitree’s Dex5or electronic skin in Linker’s hand). These give some ability to feel pressure distribution. Some have force/torque sensors to sense overall forces at the wrist or fingertips, which helps gauge weight and slip. But the “density” of sensing is far below a human’s. Also, sensing modalities like temperature or pain (like detecting harmful overload) are not common – though robots do have current monitors that serve a bit like pain sensors (if current spikes, they know they’re straining). Electronic skin research is aiming to give robots something akin to our sense of touch, including even detecting gentle touches and shear forces. In some labs, robots can feel a light brush or identify textures by touch. But those are still experimental compared to every human hand having that ability from birth. Another sense: proprioception – humans know finger positions without looking, thanks to muscle and joint sensors. Robot hands definitely have that via encoders; in fact they may even be more precise in knowing joint angles (to fractions of a degree). So in proprioception, robots are quite good – they know where their fingers are as long as sensors are calibrated (a human can be a bit uncertain of exact angle unless touching something). However, the human brain integrates all touch and position info seamlessly, whereas robotic systems are just starting to do so through AI algorithms. So currently, a human hand in terms of sensory richness and integration is still superior. For example, if you close your eyes and fish for a pen in your bag, your hand’s sense of shape and texture helps you find it. A robot might struggle without vision. But progress is being made: tactile sensor + AI combinations have enabled robots to manipulate objects just by touch (like OpenAI’s Dactyl that solved a Rubik’s cube with a Shadow Hand relying on touch feedback). These are singular feats but show that given enough sensors and compute, robots can begin to approximate human touch-based skills. By 2026, we’ll likely see more “electronic skin” deployments, but uniform full-hand coverage at human resolution (which would be thousands of sensors) will probably still be rare due to complexity and cost.

Dexterity and Skill: Perhaps the biggest remaining gap is the fluidity and adaptability of human hand movements. A skilled person can perform incredibly dexterous acts: a surgeon tying tiny sutures, an artist sketching, a musician playing guitar – all involve subtle coordinated finger motions, pressure control, and feedback response. Robots are far from matching such skills in unstructured scenarios. They can be programmed or trained to do specific tasks very well (like a robot hand can probably be taught to play a simple piano piece or solve a cube), but general dexterity – the ability to handle anything new gracefully – is still a human forte. Humans also have intuitive grasp planning; we effortlessly figure out how to grab something new, whereas robots often need calculation or trial and error. However, advanced AI is starting to narrow this gap: with deep learning, robots can learn more human-like strategies for hand use. Sanctuary’s philosophy explicitly is that “dexterous capability is directly proportional to the size of the addressable market”– acknowledging that hands that can do more things make robots more broadly useful. They and others are feeding robots huge amounts of human demonstration data, hoping to imbue them with human-like manipulation strategies. So we might see robots become surprisingly adept at everyday manipulations, even if not surpassing a human expert.

Speed and Reaction: Human hands can type about 120 words per minute on a keyboard, or do rapid sequences of tasks (like a chef chopping and then stirring). Current robot hands, even if they have the DOF, often operate at slower speeds due to motor limits or caution to avoid overshooting (though some like Optimus catching a ball show high-speed capability in controlled moves). Reaction time – a human reflex if a glass slips, fingers tighten within tens of milliseconds – robots can match this in control loop (some can react at 1kHz, i.e., every 1 ms for adjustments). So in theory, a robot might even react faster (no neural delay, just electrical signals). But practically, software and sensor processing add latency; humans still often outreact robots in real-world settings. That said, Tesla’s ball-catching demo had near-zero latency teleoperation, showing that with direct control, the physical hand could move quick enough to snatch a tennis ball. That indicates that mechanically, the hands can be as quick as a human hand or quicker; it’s control and perception that need to catch up to fully utilize that speed. Over time, AI improvements (like vision models at 10 Hz and action at 200 Hz as Figure claims) will give robots faster situational awareness and reflex. In sum, robot hands can achieve human-like speed in specific tasks (catching, perhaps tossing and re-grasping an object), but general rapid multitasking is not yet at human level.

Versatility: A human hand can go from cracking nuts with a nutcracker to gently petting a kitten, to writing with a pen, to opening a combination lock – all in the same day, same hour even. Most robot hands today, while increasingly capable, are usually optimized or programmed for a subset of tasks. They might need reprogramming or might not have the right fine tool use ability for certain things. However, the whole idea of these humanoid hands is to be general-purpose. Already, we see Optimus using one set of hands to perform varied factory chores, and Digit’s hands evolved to handle both boxes and finer actions. The versatility gap is narrowing. But a key difference: adaptability. Humans learn new hand tasks extremely well (children learn to use new tools or play new instruments given time). Robots still largely do what they’re taught – but with AI’s progression, robots are starting to learn new manipulations via practice or observation (like robotic learning of how to open different door types). By end of 2026, robots may close in on human versatility for a good range of everyday tasks, but the long tail of weird, unfamiliar tasks still favors humans. For instance, if given a completely novel puzzle or a very delicate antique to handle, a human’s intuition and careful approach is hard to beat.

Durability and Healing: As mentioned, humans heal and have some degree of self-maintenance (skin regrows, small cuts seal). Robot hands, if damaged (a torn tendon cable, a broken finger link), must be repaired by humans. Humans also sense pain as a warning to back off; robots have overload sensors but they might not always prevent damage if programming is off. Over time, improved materials (carbon fiber, self-lubricating joints) and redundancy (maybe algorithms that detect wear before failure) will help robot hands last. But one could say human hands, despite being biological, are quite robust – they handle rough tasks and usually keep working (with minor injuries now and then).

Expressiveness: Slightly aside from raw dexterity, the human hand is an instrument of communication – gestures, sign language, emotional expression (a trembling hand, a wave, etc.). Robot hands are typically not used for expressive purposes yet, but with humanoids aimed at working with people, this may become relevant. A few robots like Engineered Arts’ Ameca use hand gestures to appear more lifelike in conversation. As robot hands become more human-like, they’ll likely be used to communicate as well (pointing to objects, giving a thumbs-up, etc.). In this area, once the mechanics are there, it’s more a software/cultural challenge – teaching robots when and how to gesture meaningfully. Humans do this intuitively. So there's a social dexterity component where humans remain superior for now.

Bottom line: By the end of 2026, robotic hands will in many ways parallel the human hand’s capabilities on paper – similar joint ranges, the ability to perform many of the same tasks, and in some cases even exceeding human limits (superhuman strength or endurance, or extra DOF as in Linker’s case). However, the seamless integration of all those abilities is still what sets the human hand apart. The human hand, guided by our brain and senses, is remarkably adaptive, switching between tasks and force levels with ease, whereas each robot hand typically excels in a narrower band at a time (e.g., one might be great at heavy lifting with less finesse, another at fine work but not high force). Yet, the gap is closing fast.

In fields like manufacturing, a state-of-the-art robotic hand can now relieve a human in doing repetitive precision tasks (like sorting small components) with nearly human dexterity. In a controlled environment, a robot hand can even beat human consistency (never getting tired or shaky). But if you put a robot and a human in a messy room and ask them to clean up everything – sort objects, decide where things go, handle fragile vs heavy items appropriately – a human adult would currently still far outperform the robot in speed and judgment. By 2026, robots will have made strides but likely still fall short in such unstructured scenarios.

One telling comparison is Musk’s comment that the human hand’s complexity required Tesla to design each tiny actuator and gear custom and that manufacturing it at scale was “100 times harder” than designing it. It underscores that replicating the human hand is an enormously challenging engineering problem, but one that is being actively tackled. The good news is that each year of R&D yields improvements. The very fact we’re comparing degrees of freedom and sensor arrays shows how far robotics has come.

In summary, robot hands in 2025 are roughly where human hands are in terms of mechanical function on many axes, and they are rapidly catching up in sensor and control capabilities. They can match humans in certain individual tasks (sometimes even outperform, e.g., a robot hand might hold a precise position longer without fatigue, or survive in a hazardous environment a human hand can’t). But the human hand, with its incomparable combination of fine control, sensory richness, and adaptability, remains the benchmark and still ahead in overall versatility and subtlety. The coming years will likely see that benchmark approached ever more closely – as one researcher quipped, the hand is “the toughest problem” but one that’s being progressively solved. And as robotic hands gain ground, we’ll find them working alongside human hands, each doing what they do best – with robot hands taking over tedious or dangerous tasks, and human hands continuing to excel at the highest-skill and most creative manipulations.

Sources: The information above was synthesized from a variety of sources including tech press releases and expert analyses

Sources

• Tesla – Optimus humanoid robot overview
• Figure AI – Figure 01 humanoid platform
• Agility Robotics – Digit humanoid robot
• Apptronik – Apollo humanoid robot
• Sanctuary AI – Phoenix humanoid robot
• Shadow Robot Company – Dexterous Hand
• Hanson Robotics – Humanoid hand design in Sophia
• Soft Robotics Inc. – Adaptive robotic grippers
• RightHand Robotics – RightPick gripper technology