1. Mechanical Architecture

Finger Articulation and Degrees of Freedom (DoF): The Optimus humanoid robot features an anthropomorphic hand with five fingers (four fingers plus an opposable thumb) inspired by human anatomy. In Tesla’s first-generation design, the hand has 11 degrees of freedom (DoF) driven by 6 actuators, with joints arranged to mimic fundamental human finger motions. Each finger has multiple joint segments enabling flexion/extension, allowing a wide aperture span for power grasps as well as precise pinching of small objects. However, the initial Optimus hand omitted certain motions: an expert analysis noted there was “no finger ab/adduction” (no lateral spread of fingers) and the thumb had only 2 DoF, limiting human-level dexterity. Even so, Optimus’s Gen1 hand already ranked among the most dexterous 5-finger robot hands, with substantially more DoF (11) than many competitors that had only 6–7 DoF. Tesla has since developed an upgraded hand (Gen3) with 22 DoF, approaching human complexity. This roughly doubles the joints per finger (adding independent joint control and lateral movements) and brings the total hand DoF closer to a human hand’s ~25+ joints. Table 1 summarizes the finger joint degrees of freedom in the Optimus hand compared to a human hand.

Actuation Method – Servomotors and Tendon Drives: All of Optimus’s hand joints are electrically actuated, using miniaturized servo motors combined with high-ratio gear reductions and tendon linkages. Tesla’s engineers implemented a tendon-driven architecture reminiscent of biological extensor/flexor tendons. Metallic cable tendons route through the finger linkages to transmit force from motors housed remotely in the forearm. According to a Tesla patent and third-party analyses, the Gen3 hand uses custom planetary gearboxes and ball-screw linear actuators embedded in the forearm, converting motor rotation into linear tendon pull. This design is analogous to a human, where muscle bellies in the forearm pull tendons to move the fingers. By relocating all finger actuators to the forearm, the hand itself is lightweight and compact. (The latest Optimus hand indeed “has tendons, much like a human hand” with all actuation moved to the forearm.) Each finger’s flexion is driven via these tendon transmissions, and stiff spring elements around the tendons help compensate for any slack or elongation during wrist motion, maintaining precise control. The range of motion achieved is designed to mimic human finger curl; Optimus can fully open and close its hand, performing wide grasp postures (spanning large objects) down to a tight pinch grip on small, thin items. The joint speed is high as well – humans can move fingers at ~300°/s, and Optimus targets comparable speeds.

Non-Backdrivable Clutch and Force Control: A notable mechanical feature in Optimus’s fingers is a clutch mechanism on the flexor drive. This clutch can decouple the finger from the actuator, making the joint non-backdrivable – meaning once a grip is achieved, the fingers lock in place without continuous motor effort. This allows Optimus to hold objects securely (even for extended durations) with zero power consumption on the hand motors. It also protects the drive from back forces, improving robustness during heavy object manipulation. However, because the harmonic drives and screw actuators have high friction and gearing, the system is not naturally backdrivable or “transparent” to external forces. Tesla compensates for this by running active force-feedback control loops at each joint, effectively simulating compliance via software. The clutch plus closed-loop control enables a balance of strength and finesse: Optimus’s hand can apply significant grip force when needed but also relax or absorb shocks to avoid damage.

Overall Dexterity: While the first-gen Optimus hand had limitations (rigid finger grouping and limited thumb mobility), it still demonstrated dexterity sufficient for many tasks. NVIDIA’s Jim Fan noted that Optimus Gen1 had one of the best five-finger hands in robotics – combining multi-DoF kinematics with robustness to withstand extensive use without frequent maintenance. The forthcoming 22-DoF upgrade substantially narrows the gap to human hand capabilities, potentially adding independent finger splay (abduction/adduction) and a more opposable thumb. Indeed, Elon Musk suggested the new hand could even perform complex maneuvers like playing musical instruments. Table 1 compares the finger joint DoF for Tesla’s Optimus (initial and latest) versus a human hand. Even with slightly fewer total joints than a human, Optimus’s upgraded hand is expected to achieve human-like grasp versatility, from power grips around large tools to delicate two-finger pinches.

Table 1. Joint Degrees of Freedom (DoF) per Finger in Tesla Optimus Hand vs Human Hand

Digit	Optimus Hand (Gen1)	Optimus Hand (Gen3)	Human Hand (approx)
Thumb	2 DoF (limited opposition)	3–4 DoF (improved opposable motion)	~5 DoF (multi-axis base + 2 joints)
Index	2 DoF (flexion only)	~3–4 DoF (independent joints + lateral motion)	~4 DoF (MCP flexion/abduction + 2 joints)
Middle	2 DoF (flexion only)	~3 DoF (independent joints)	~4 DoF (including slight ab/adduction)
Ring	2 DoF (flexion only)	~3 DoF (independent joints)	~4 DoF (including slight ab/adduction)
Little	2 DoF (flexion only)	~3–4 DoF (independent joints + lateral motion)	~5 DoF (added mobility for opposition)
Total Hand	11 DoF	22 DoF	25–27 DoF (full human hand)

(Notes: DoF counts for Optimus Gen3 are inferred from total 22 DoF; human hand DoFs vary by definition. MCP = metacarpophalangeal joint.)

2. Materials and Manufacturing

Structural Materials – Strength vs. Weight: Tesla carefully selected materials for the Optimus hand to maximize strength-to-weight ratio and durability. The skeleton of the hand (the finger segments and palm frame) is believed to be made from lightweight metal alloys, likely high-strength aluminum or magnesium alloys, similar to those used in robotics and aerospace. These metals provide rigidity and fatigue resistance under repetitive motion while keeping mass low. Indeed, Optimus as a whole is built from “lightweight materials” to keep the 1.73 m, 57 kg robot as light as possible. High-stress components such as gear teeth and bearings in the finger actuators are almost certainly made of hardened steel for longevity. The tendons that drive the fingers are metallic cables – Tesla specifically noted the fingers use “metallic tendons to allow for flexibility and strength”. Steel cable tendons can endure many bending cycles and high tension without significant stretching, ensuring reliable transmission of force. To offset any small elongation or slack in these tendons, Tesla’s Gen3 design wraps stiff springs around them (analogous to bicycle brake cables), maintaining tension and positional accuracy. Joint pins and actuator shafts are likely steel as well for maximum wear resistance.

Despite seeking performance, Tesla’s design philosophy emphasizes manufacturability and cost-efficiency at scale. One Tesla engineer remarked that unlike a one-off research robot, Optimus cannot rely on exotic materials like carbon fiber or titanium; instead it must be made from cost-effective materials (even “plastic things” in some areas) to allow mass production of thousands or millions of units. This suggests that non-structural covers and some internal components may be injection-molded engineering plastics or composites rather than all metal. For example, finger linkages might incorporate engineered polymer bushings or lightweight composite housings where feasible, reducing weight and simplifying manufacturing. Tesla likely leverages its automotive materials expertise (high-volume aluminum casting, etc.) to produce Optimus’s components efficiently. The hand’s metal skeletal parts could be made via precision CNC machining or die casting of aluminum, achieving both accuracy and scalability. By designing around cheaper materials and slightly lower stiffness, Tesla accepts a lower structural frequency target (the robot’s limbs can flex a bit under load, similar to a human’s compliance) in exchange for huge cost savings. This trade-off is managed through clever control algorithms that maintain stability even with more compliant materials.

Gripper Surface and Elastomers: A critical material aspect for any robotic hand is the surface that contacts objects. Tesla’s initial Optimus hand, as observed by experts, had “bare metal hands” with essentially no soft covering. This is not ideal for gripping – rigid metal fingertips can easily slip or break fragile objects (picking up a glass with “two metal spoons” is very difficult). Acknowledging this, Tesla added a soft protective layer on the new generation of fingers and palm. This layer is likely made of a silicone or urethane elastomer, similar to synthetic rubber “skin”. Such an elastomer provides a compliant, high-friction contact surface that improves grip on smooth or irregular objects and cushions impacts. It also mimics the cushioning of human finger pads, which is important for tactile sensing fidelity. Tesla’s team noted the challenge of adding “squishiness/compliance and a protective layer on the fingers and palm, without affecting tactile sensing too much”. In other words, the silicone skin must be tuned – soft enough to be effective, but thin/firm enough that underlying sensors still accurately feel pressure. The solution might be a thin silicone pad with embedded sensor arrays (forming an electronic skin), or a multi-layer construction that preserves sensitivity. By late 2024, Tesla demonstrated the new hand with a realistic soft outer layer, vastly improving its ability to securely grasp delicate items (for example, handling an egg without cracking it was shown).

Actuator Manufacturing and Advanced Techniques: The Optimus hand’s actuators (both rotary harmonic drives and linear screw drives) are custom-designed by Tesla. These compact actuators integrate not only the motor and gearbox, but also sensing and control electronics within the assembly. Manufacturing such actuators in quantity requires advanced techniques. Tesla likely uses precision machining for the harmonic drive components (wave generators, flexsplines) and ground ball screws for the linear actuators, to achieve the tight tolerances necessary for smooth operation. They may also utilize additive manufacturing (3D printing) for rapid prototyping of actuator housings or lightweight lattice-structured parts, though production units would switch to casting or molding for efficiency. Given Tesla’s automotive background, automated assembly and gigacasting-inspired processes could be applied: for instance, casting the entire forearm frame that holds the actuators as one piece for strength and alignment, or overmolding wiring harnesses into structural pieces to simplify assembly.

Furthermore, each hand has an in-hand controller PCB (printed circuit board) that manages the motors and sensors. This likely uses Tesla’s in-house electronics manufacturing capabilities, producing compact, robust boards similar to those in cars. The fingertips’ tactile sensor arrays (if based on piezoresistive or capacitive technology) might be fabricated with MEMS techniques or printed flexible circuits. These would then be encapsulated in the silicone fingertip pads. Such integration of electronics with soft materials is cutting-edge, akin to emerging “electronic skin” tech. Overall, Tesla is combining traditional high-strength materials (metals) with modern polymers and electronics in the Optimus hand. The engineering rationale is clear: achieve human-like hand performance (strength, dexterity, touch) while meeting automotive-grade reliability and being manufacturable at scale. This involves careful material choice for each subsystem – strong alloys for structural and moving parts, fatigue-resistant cables and springs for tendons, and compliant polymers for surfaces – all assembled with innovative manufacturing to keep costs in check.

3. Sensory Integration and Control

Tactile Sensors and Touch Sensing: To manipulate objects as deftly as a human, Optimus’s hand is equipped with tactile sensors at key touch points. Tesla confirmed that the Gen1 hand had sensors in its fingertips, providing sensory feedback when grasping items. These are likely pressure or force sensors embedded just beneath the fingertip surface. Early robot hand designs often use piezoresistive or piezoelectric sensor pads in each finger pad, which can detect contact forces and shear (slip). Optimus’s tactile sensors enable it to measure how hard it is pressing on an object and detect if an object is slipping, allowing for dynamic grip adjustment. In Tesla’s latest iteration, the tactile sensing system has been extended across more surface area of the hand. Milan Kovac (Tesla’s Optimus program lead) indicated the new hand will have “much more surface coverage than the previous hand” for touch sensing. This likely means additional sensors on the finger phalanges and palm, not just the fingertips. Such distributed “electronic skin” could allow Optimus to feel contact along its fingers or even when the side of the hand brushes an object, enhancing safety and dexterity. The challenge is integrating these sensors with the soft finger pads without losing fidelity, which Tesla is addressing through materials and calibration.

Force Feedback and Proprioception: Complementing tactile sensors are internal sensors that give Optimus a sense of its own hand posture and forces – analogous to human proprioception. Each finger joint is outfitted with precise position encoders (likely optical or magnetic encoders on the motor or joint) that report joint angles in real time. This allows the robot to know where its fingers are and coordinate complex finger movements. Moreover, the actuator design likely includes torque sensing. This can be done via strain gauges on the flexure elements or by monitoring motor current combined with models of the transmission stiffness. The initial Optimus hand design required active force control at the joints, implying the control system reads forces/torques at each joint and adjusts motor output to achieve a desired force (essential for safe interactions). These internal force sensors serve as a feedback mechanism so that, for example, if a finger hits an obstacle or an object presses back, the controller can limit force to avoid damage. In the feet and larger joints of Optimus, Tesla explicitly uses force-torque sensors for balance; in the hands, the same principle applies on a finer scale for gripping. The combination of internal sensors (joint angle, motor torque) and external sensors (tactile arrays) yields a rich data stream for the control system. During a grasp, Optimus can sense contact onset (via touch sensors), measure the force distribution across the fingers, and feel the weight of the object through joint torques. This sensory feedback is used in real time to modulate grip strength – squeezing firmly but not too tightly – and to stabilize the object against disturbances.

In-Hand Microcontroller and Edge Computing: The Optimus hand contains its own dedicated processing for sensor integration and low-level control. Tesla’s engineers have built an in-hand controller (embedded computer) that “drives the fingers and receives sensor feedback” directly within the hand assembly. This local controller likely runs fast control loops (kHz-level) to process tactile signals and joint sensors, enabling reflexive responses (like stopping closure when an object is grasped or adjusting grip if slipping is detected) without needing to query the main brain. By offloading these tasks to the hand itself, latency is minimized and the system is more fault-tolerant. The sensor data is then sent to the robot’s central processor for higher-level decision making. Optimus uses Tesla’s Full Self-Driving computer adapted for the robot – a powerful AI computer that fuses vision, proprioceptive, and other sensor inputs. Tactile and force feedback from the hands are integrated here with camera inputs (e.g. visual identification of an object) to inform the robot’s AI about what it is holding and how it should move it. This mirrors how humans use touch and sight together (feeling an object’s texture/weight while seeing its shape).

The AI and control stack for Optimus hand likely involves multiple layers: a high-level grasp planner (deciding how to grip an object based on vision and prior knowledge), a mid-level controller (setting target finger positions/forces), and a low-level motor controller (executing those targets with feedback). Tactile sensors inform the system about contact events – for instance, when fingers make initial contact, the controller can switch from position control to force control, regulating the grasp force. If an object starts to slip (detected by a change in force distribution or a high-frequency vibration on a sensor), the controller can responsively increase grip pressure to compensate, much like a human would. Tesla has stated that sensor feedback is “really important to learn a little bit more about the objects that we’re grasping”. The AI can use tactile information to infer properties like object softness, shape, or weight (for example, feeling that an object is heavier might prompt the robot to use two hands or adjust how it lifts).

Sensor Accuracy and Resolution: Specific numbers for Optimus’s sensor resolutions are not published, but we can infer they are high. Joint encoders likely provide sub-degree accuracy, enabling finger positioning on the order of 0.1° or better. Tactile sensors in robotics often can detect forces down to a fraction of a Newton. For instance, Optimus was shown gently pinching and lifting an egg without cracking it, which implies the hand can regulate forces to perhaps under 1 N of precision. The tactile sensing array might have a grid of taxels (tactile pixels) in each fingertip, giving a spatial resolution fine enough to detect contact location and perhaps even discern some texture. However, current tactile technology often trades off spatial resolution for robustness – many robotic fingertips use a small number of force cells or a continuous pressure sensor covering the pad. It’s likely Optimus’s fingertip sensors can at least distinguish the center of pressure and total normal force on each pad. As research progresses, Tesla could adopt more advanced multimodal sensors (sensing pressure, shear, and even temperature) to more closely mimic human touch. For now, the Optimus hand’s sensing suite (encoders + force sensors + fingertip tactiles) provides a comprehensive feedback loop so that the AI knows exactly how the hand is interacting with objects in real time. This integration of sensing with control is what allows fine maneuvers like threading a needle or handling fragile items, as well as enabling autonomous learning – the robot can feel the outcome of its actions and adjust accordingly, improving its manipulation skills over time.

4. Comparative Engineering Analysis

To put Tesla’s Optimus hand in context, it’s useful to compare it with the hand designs of other cutting-edge humanoid robots: Boston Dynamics’ Atlas, Honda’s ASIMO, and Agility Robotics’ Digit. Each of these systems has a distinct design philosophy, especially regarding the end-effectors (hands), actuation approach, and sensor sophistication. Table 2 summarizes key comparisons among these robots’ hand designs, followed by detailed observations:

Boston Dynamics Atlas: Atlas is famous for its athletic agility and dynamic moves, enabled by its hydraulic actuation and advanced control. However, Atlas’s hands are relatively simple compared to Optimus. Early Atlas models (circa 2015) often used rudimentary two-finger or three-finger grippers primarily designed for robustness in the DARPA challenge tasks (turning valves, opening doors). The current Atlas can be outfitted with different “gripper variations” for handling objects, but these are closer to parallel-jaw grippers or mitt-like claws than true five-fingered hands. Atlas’s total degrees of freedom is about 28 (for the whole robot), and its hands likely account for only a handful of those (on the order of 1–2 DoF per hand for open/close and maybe wrist pitch). In practice, Atlas has demonstrated impressive manipulation of large objects – for example, tossing a heavy tool bag in a recent demo – but this is achieved through arm trajectory planning and simple gripper action, not fine finger articulation. The design emphasis for Atlas’s end-effectors is durability and grip strength to hold heavy or awkward objects during dynamic motions, rather than delicate dexterity. Actuation strategy: Atlas’s hydraulics allow very high force output (each joint cylinder can exert large torques), so its grippers can apply strong clamping forces. However, hydraulics also make it challenging to implement many independent small joints in the hand due to complexity and space – hence BD keeps the hand simple. Sensors: Atlas certainly has force-torque sensors in its wrists and load sensors in its grippers to know how hard it’s squeezing. It also uses vision (stereo cameras) for locating objects. But it does not have advanced tactile sensing on the fingers as far as public info indicates. The comparative result is that Optimus’s hand far exceeds Atlas’s in dexterity and human-likeness, while Atlas relies on raw power and whole-body coordination to achieve its tasks. An industry analysis pointed out this differentiator: Tesla’s Optimus has “dexterous hands, which Boston Dynamics does not have”, giving it an edge in fine manipulation.

Honda ASIMO: Honda’s ASIMO (especially the last generation unveiled in 2011) was a pioneer in humanoid hand dexterity. The new ASIMO featured multi-fingered hands with 13 DoF per hand – that’s independent control of each of five fingers (multiple joints each). This gave ASIMO the ability to perform intricate tasks: famously, its hands were dexterous enough to do sign language, unscrew bottle caps, and hold a paper cup without crushing it. Honda achieved this with electric servo actuators driving the fingers, likely using a combination of small motors and linkage mechanisms housed in the forearms and palms. ASIMO’s fingers had independent finger control with joint angles monitored in real time. It also integrated tactile sensors in the fingertips and palm. By fusing touch sensors with visual input from its cameras, ASIMO could, for instance, grasp a bottle it sees, feel when it has a firm hold, then twist the cap off without dropping the bottle. In engineering terms, ASIMO’s approach to the hand was very similar to Tesla’s: an anthropomorphic design with multi-DOF, electrically actuated fingers and sensor feedback loops. One difference is that ASIMO was built in an era of less powerful computing; its grip was somewhat pre-programmed for specific tasks. In contrast, Optimus’s hand, supported by modern AI, aims for more generalized skill learning. Actuation: ASIMO’s fingers likely used tiny DC motors with reduction gearing. Given space constraints, some joints may have been coupled or tendon-driven. (A research paper noted a hydraulic master-slave system for finger joints in an earlier Honda prototype, giving 13 DoF per hand – but production ASIMO used electric drives.) Dexterity: In terms of pure joint count and capability, ASIMO’s 13-DoF hand is comparable to Optimus’s 11-DoF first-gen hand, and slightly behind Optimus’s upcoming 22-DoF hand. But ASIMO set the benchmark in its time: it demonstrated that a humanoid robot could have human-like hands that perform complex manipulations. Tesla is effectively extending that legacy with newer technology. Notably, ASIMO’s advancements showed the value of compliance and sensing in hands – its ability to handle a cup without crushing it came from carefully tuned finger forces and tactile feedback. Optimus builds on that with even more sensors and modern computing to potentially exceed ASIMO in dexterity.

Agility Robotics Digit: Agility’s Digit is a bipedal robot aimed at practical tasks like warehouse logistics (carrying boxes, unloading trucks). Unlike Optimus, Digit currently forgoes an elaborate human-like hand in favor of simpler end effectors. Digit’s arms end in “claw-like” grippers with 2 fingers (opposing paddles) that open and close. These grippers serve to hook onto bins or grasp the sides of packages, prioritizing reliability and ease of control. Digit’s design philosophy is pragmatic: since its primary jobs involve moving medium-to-large objects (totes, boxes), a complex hand might be unnecessary and could complicate the robot’s stability or cost. Indeed, Agility’s leadership has quipped that none of the effective industrial solutions use “five-fingered, 27-DoF hands” and that some competitors only add human-like hands as a “branding exercise”. This was a thinly veiled reference to Tesla’s approach, emphasizing that Digit’s focus is on swappable end-of-arm tools for the task at hand rather than a do-it-all human hand. Actuation: Digit’s arms (including the gripper) are fully electric, using servo motors. The gripper likely has a single motor driving both fingers in a parallel motion (1 DoF per hand). This gives a firm grip on rigid objects but limited adaptability. The upside is simplicity – fewer actuators means less to control or break. Sensors: Digit probably has basic grip force sensing (perhaps via motor current or a load cell) to avoid crushing objects and to detect when it has picked something up. It uses vision and depth sensors to guide its arms to objects, but it does not have intricate fingertip tactile sensors or finger proprioception like Optimus does. Dexterity: In comparative terms, Digit’s hand is the least dexterous of this group – it cannot perform fine motor tasks like turning a key or typing. Its hands cannot conform to objects of varying shapes as a five-finger hand can. However, for Digit’s intended applications (boxes, crates, tools with handles), the two-finger gripper is sufficient and actually faster to control. Agility Robotics envisions possibly offering swappable hands for different jobs (e.g., a suction gripper for picking flat packages, or a more dexterous hand for specific tasks), but the base model is kept simple to ensure robustness in industrial settings.

In summary, Tesla’s Optimus hand stands out for its human-inspired complexity and sensory richness. Where Atlas sacrifices finger dexterity for brute-force grippers, and Digit chooses simplicity for reliability, Optimus pursues a full human-hand replication to unlock general-purpose manipulation. It is closest in spirit to Honda’s ASIMO hand, but benefits from a decade of technological progress in actuators, sensors, and AI. This should allow Optimus to achieve equal or greater dexterity (22 DoF with advanced control vs. ASIMO’s 13 DoF) and to do so in a more autonomous, learning-driven manner. The true measure of dexterity will be in tasks: Optimus is being trained (via teleoperation and AI) on fine assembly and daily tasks, whereas Atlas showcases parkour and heavy lifting, and Digit focuses on cargo handling. The comparative metrics are captured in Table 2, highlighting each platform’s design trade-offs in hand engineering.

Table 2. Comparison of Humanoid Robot Hand Designs (Optimus vs. Atlas vs. ASIMO vs. Digit)

Robot	Tesla Optimus (Gen2/Gen3)	Boston Dynamics Atlas	Honda ASIMO (2011)	Agility Digit
Hand Design	Five-finger anthropomorphic hand; opposable thumb. Upgraded Gen3 hand closely mimics human anatomy with tendon-driven fingers.	Originally simple two- or three-finger grippers (tool-like claws). Newest Atlas uses swappable gripper tools, not a full human-like hand.	Five-finger anthropomorphic hand, human-sized. Independent finger control enabling complex gestures (e.g. sign language).	Two-finger “claw” gripper (parallel jaws). Designed for hooking and clamping onto boxes/bins, not individual finger articulation.
Hand DoF	11 DoF (Gen2) increasing to 22 DoF (Gen3) in hand alone; plus 3 DoF in wrist. Each finger has multi-joint flexion; new hand adds lateral/thumb mobility.	Est. ~2–3 DoF per hand (open/close and minimal wrist bending). Atlas total 28 DoF mostly in limbs. Fingers mostly fixed shape; limited articulation.	13 DoF per hand (independent joints for all 5 fingers). Nearly human-level finger joint count (e.g., 4 joints/thumb, 3 per other fingers).	~1 DoF per hand (finger opening/closing together). Wrist may add a couple DoF for arm use, but no finger-level independence.
Actuation	Electric servo actuators with strain-wave gears and ball-screw drives. All finger actuators in forearm pulling steel tendons (high-force, compact). Non-backdrivable clutched joints hold load without power.	Hydraulic actuators (high-pressure fluid cylinders) for all joints. Gripper typically a passive clamp or simple hydraulic/pneumatic jaw. Very high force output, but low compliance in fingers.	Electric servos (DC motors) in arms/hands with gear reductions. Some tendon or linkage mechanisms in forearm to drive finger joints. Emphasis on smooth, precise motor control for each finger.	Electric motors driving simple linkages. Likely one motor per gripper that drives both fingers symmetrically. Some spring compliance may be present to maintain grip.
Grip Strength	Capable of both delicate and strong grips. Can hold fragile items (egg) with <1 N force, yet designed to lift tools or 20 kg objects (with two hands) – strong actuators and locking clutches provide human-level grip force.	Extremely high potential force (hydraulics) – can exert large clamping forces (tens of kg). Used for heavy/bulky object handling. Not designed for light touch (no fragile item handling demonstrated).	Sufficient grip force for household objects – could pick up a bottle, serve a drink, carry a tray. Force limited by motor torque to avoid crushing items. Not built for heavy lifting, but very adept at moderate force with control.	Strong enough to carry ~15 kg boxes in warehouse scenarios. The gripper’s two-finger design focuses force on edges of objects; effective for rigid loads but not adaptable to very small or soft items.
Sensors	Tactile sensors on fingertips (and expanded across fingers/palm in newest version). High-res encoders on every joint for position; torque/force sensing in actuators (for active force control). Full integration with vision and AI for object recognition.	Rich proprioceptive sensors (joint positions, IMUs) for balance; wrist force-torque sensors for interactions. Lacks tactile sensing on gripper fingers; relies on vision and predefined force limits. Vision (stereo cameras, LIDAR) used for locating objects.	Tactile and force sensors in fingers combined with stereo vision. Could detect object presence and grip force to avoid crushing. Joint angle sensors on all fingers for feedback. Onboard AI (less advanced than Tesla’s) coordinated vision & touch for specific tasks.	Basic proprioception (joint angles, motor current). No finger tactile array – primarily uses cameras and possibly wrist force sensors to gauge grasp. Focus is on reliable pickup, not sensitive manipulation. Agility prioritizes external vision/AI for guiding the gripper to targets.
Notable Skills	Demonstrated: Sorting objects on shelves, picking up and assembling car parts, using tools, and folding fabrics. Can transition from power grips (lifting a heavy battery) to precision handling (plugging in connectors, handling eggs) in the same session. Teleoperation used for data collection, with AI learning to replicate human-like hand use.	Demonstrated: Carrying heavy objects, shoveling, and construction-style tasks in demos. Throws and catches heavy packages in latest videos. Excels at dynamic moves (jumping while holding objects). Not shown doing fine detail tasks like typing or picking up tiny items – hand is mainly for secure grasp of larger objects during acrobatics.	Demonstrated: Turning valves, opening bottles, pouring drinks, carrying a tray of drinks to serve humans. Could shake hands and gesture in sign language. Highly coordinated bimanual tasks (unscrewing a jar while holding it). Limited autonomy (scripted demos), but mechanically capable of delicate operations in structured settings.	Demonstrated: Lifting and moving boxes/totes in warehouses, unloading bins, placing items on conveyor. Repetitive pick-and-place of moderate loads with high reliability. Does not perform individual finger tasks. Future plans include swappable end-effectors for different jobs (e.g. suction gripper), rather than evolving a single dexterous hand.

(Sources: Tesla/Optimus specs; Atlas and BD info; Honda ASIMO specs; Agility/Digit info.)

5. Applications and Functional Testing

Current Capabilities in Manipulation: Tesla’s Optimus hand has been undergoing extensive testing in both laboratory and real-world mockups, demonstrating a range of manipulation tasks. Internally, Tesla has a “data collection farm” for Optimus where multiple robots are teleoperated to perform various chores and industrial tasks, training the AI through repetition. Video footage released by Tesla (and dissected by experts) showcases Optimus using its hands for factory tasks like moving EV battery modules, operating assembly machines, and sorting objects on shelves. In one scenario, Optimus delicately handles pieces of laundry – a task requiring fingers to grasp soft, limp fabric without snagging or dropping it. In another, it lifts and carries a large battery, which tests the grip strength and the coordination of both hands together to distribute weight. The robot has been seen picking up small tools and parts, fastening them or placing them with precision. These tests indicate an impressive versatility: Optimus’s hands can deal with a wide spectrum of object sizes, weights, and stiffness. The ability to go from firmly gripping a 5–10 kg object to gently manipulating a fragile item highlights the hand’s dynamic range in force and control.

A particularly striking demonstration of Optimus’s fine control is the egg-handling test that emerged with the Gen2 hand. The humanoid can pick up a raw egg between thumb and index finger, move it, and set it down without cracking it. This task validates the effectiveness of the tactile sensors and force-control algorithms – the hand can apply just enough pressure to hold the egg securely while continuously monitoring and limiting the force to avoid exceeding the shell’s threshold. Very few robots have achieved this level of delicacy in unstructured demos. It implies that each finger can modulate force in small increments (<1 N) and respond within milliseconds to any spike in pressure. On the other end of the spectrum, Elon Musk noted the upgraded Optimus should be able to “lift a pipe in a car factory” or handle power tools, which speaks to the hand’s strength and durability. The clutch mechanism allows the robot to support heavy parts for long durations by locking the grip without straining the motors.

Precision Handling and Tool Use: Engineers evaluating Optimus have observed it performing precision assembly tasks. For example, during Tesla’s AI Day demonstration, Optimus used its fingers to precisely plug in a cable connector and fasten screws on a small component. Such tasks require not only fine motor control but also coordination between both hands – one hand might hold an object while the other manipulates a tool. Optimus’s human-like hand design and dexterity make it naturally suited to use human tools: it can grip a drill or screwdriver handle and operate it, or press buttons and flip switches that are designed for human fingers. In lab tests, Optimus reportedly practiced using wrenches and even typing on a keyboard. With 11 DoF and tactile feedback, each finger can conform around tool handles and apply appropriate forces as a human hand would. Competing humanoids like Atlas or Digit, with their simpler grippers, struggle with such nuanced interactions (they cannot, for instance, easily turn a knob or pick up a coin from a table). Optimus’s advantage is evident in tasks like assembling small parts: it can pinch and place tiny components (nuts, bolts, electronic parts) with millimeter accuracy. Sensor feedback ensures that if a part is slightly misaligned, the robot can feel the contact and adjust – a critical ability for any assembly work.

Range of Motion and Human Interaction: The Optimus hand’s range of motion, approaching that of a human, allows it to perform gestures and potentially interact with humans in intuitive ways. It can give a (gentle) handshake, wave individual fingers, or signal with a thumbs-up. These are more than gimmicks; in collaborative environments a robot that can gesture or handle objects the same way a person would is easier to work alongside. During tests, Optimus has been shown handing objects to human workers and receiving items from them, indicating sufficient compliance and safety in its grip to not harm the human. The many joints in the hand confer the ability to reorient objects within the hand, a subtle but important skill. For example, Optimus can pick up a pen in an awkward orientation, then finger-roll or adjust the pen between its fingers to a proper writing position – something a simple two-finger gripper cannot do without external help. Such in-hand manipulation is a key metric for dexterity; it requires coordinated control of multiple finger joints simultaneously, along with friction management. Optimus’s multi-DoF fingers and real-time control make this possible.

Robustness and Fatigue Testing: From an engineering perspective, Tesla is rigorously testing the hand for durability under real-world conditions. The hand’s robustness was emphasized by experts who noted it can withstand lots of object interactions “without constant maintenance” – a crucial requirement for a production robot. Tesla likely runs cycles of the hand gripping and releasing objects tens of thousands of times to ensure the tendons and joints hold up (fatigue resistance of the tendons is an important design metric). Videos have shown Optimus falling or catching itself; the hands may occasionally absorb impact, so they are built with structural reinforcement (and that soft rubber layer helps as a shock absorber too). Additionally, safety testing is performed: the control system ensures that if a finger encounters an unexpected obstacle (like a human hand in the way), the force is limited so as not to pinch dangerously. The plethora of sensors means the robot can detect anomalies (like if an object breaks in its grasp or if it picks up something heavier than anticipated) and respond appropriately, either by adjusting grip or aborting the action.

Comparative Performance in Demos: Compared to Atlas and Digit, which have had very flashy mobility demonstrations, Optimus’s public demos have been more modest but focused on real-life utility tasks. While Atlas leaps across platforms with a simple gripper, Optimus walking and doing pick-and-place in a factory setting is arguably closer to a useful application. Observers have noted that Tesla’s approach is to solve manipulation and useful work first, even if movement is slow and steady. In a recent demo (at Tesla’s ‘We Robot’ event), Optimus was teleoperated to perform a series of assembly-line tasks: it walked to a table, picked up components, inserted them into a fixture, fastened screws, and checked the result – all using its hands for the fine actions. The impressive part was not speed (it was deliberate), but that the robot could complete each step with the same hardware hand – showing versatility. Atlas, by contrast, might perform one heroic stunt per demonstration, and Digit sticks to carrying pre-defined loads. As of 2025, Optimus’s hand is perhaps the most human-like and capable among general-purpose humanoids, according to outside experts like Jim Fan, who placed it “among the best” in the world.

Projected Capabilities: Looking forward, Tesla aims for Optimus’s hands to achieve even greater autonomy and skill. With the ongoing integration of more tactile sensing and refined tendon control, future iterations should handle even trickier tasks – for instance, tying a knot, using a keyboard and mouse, or picking fruits (a task requiring just the right squeeze). Musk has even suggested that a sufficiently dexterous Optimus could play musical instruments – a high bar for hand coordination. While that remains aspirational, the technical trajectory (doubling DoF, adding sensing, improving control bandwidth) supports continual improvement in dexterity. In practical terms, within a few generations Optimus might achieve near-human hand performance: meaning it could take over not just repetitive factory work, but also delicate tasks like electronics assembly, chef-like food preparation, or assisting the elderly with daily activities (e.g. tying shoelaces, opening medication bottles). Each of those applications demands a deft touch and adaptability that only a true five-fingered design with rich sensorimotor integration can provide.

In conclusion, Tesla’s Optimus humanoid hand represents a remarkable engineering effort to replicate the mechanical complexity, material sophistication, and sensory richness of the human hand. Through iterative design – moving from an 11-DoF, 6-actuator model to a 22-DoF tendon-driven system – Tesla is pushing the boundaries of robot manipulation. Official specifications highlight human-like degrees of freedom, strength, and tactile feedback in the hand, while expert analyses underscore Tesla’s unique approach of combining raw engineering (powerful actuators, novel clutched tendons) with AI-driven control and learning. Compared to its peers, Optimus’s hand is a leap toward human-level dexterity, aiming not at a single specialized task but at a broad spectrum of activities in our daily environments. Ongoing tests already show the hand performing an array of tasks from heavy lifting to egg-cradling, validating the design choices. As the hardware continues to mature (with better materials, more sensors, and refined mechanics) and the AI software learns from more experience, Tesla Optimus’s hands are poised to set a new benchmark in humanoid robot engineering – turning decades of robotics research into a tangible helper that can use its hands as we do, to transform the world around it.