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Humanoid robots can walk, reach, and manipulate objects in demonstrations, yet still fail at tasks that look simple to people. The reason is not one missing component. A useful real-world action must coordinate locomotion, balance, perception, contact sensing, dexterous hand control, and decision-making while the environment changes. These systems are mechanically and computationally coupled, so a small error in one part can make the whole task fail.
That is why a robot’s impressive isolated movement should not be confused with general-purpose ability. Current research shows progress in each subsystem, but reliable whole-body performance across varied objects, surfaces, disturbances, and confined spaces remains an unsolved engineering problem.
The central problem: one task combines several hard problems
Humans perform many household and industrial actions as one apparently simple activity: walk to an object, stand securely, reach for it, feel its resistance, grasp it, lift it, and place it somewhere else. A humanoid robot must solve each of those stages with sensors and controllers that operate at the same time.
The body creates strong coupling. When a robot reaches forward, its center of mass moves and the forces at its feet change. When it pulls a door, pushes a box, or carries a load, the hands and arms transmit forces through the torso and legs. The controller must maintain feasible foot contacts while also keeping the hand-object contact stable. A motion that is valid for the arm alone may make the whole robot tip, slide, or exceed an actuator limit.
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Real environments add uncertainty. Object locations are not exact, friction varies between surfaces, objects can deform or slip, and a person or obstacle may disturb the robot. Perception, planning, and low-level control must update quickly enough to handle those changes. Reviews of humanoid locomotion and manipulation identify contact planning, whole-body control, sensing, learning, and computational efficiency as linked challenges rather than independent checkboxes.
Why robotic dexterity is harder than opening or picking up something
A hand has a high-dimensional action space
A human hand has many joints and can change contact patterns continuously. A robot hand with several fingers has a similarly large set of possible joint positions, velocities, and forces. The controller must choose coordinated actions while respecting the object’s shape, the hand’s kinematics, actuator limits, and collision constraints.
The 2025 survey Interactive imitation learning for dexterous robotic manipulation: challenges and perspectives—a survey describes this as a high-dimensional control problem. More joints provide potential versatility, but they also create more ways to make a slightly wrong movement. Collecting enough demonstrations or interaction data to cover those possibilities is difficult.
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Contact is partly hidden and changes during the action
Picking up a rigid object is not just a matter of moving fingers to measured coordinates. The robot needs to know whether a fingertip has actually touched the object, whether the contact is secure, and how much force is safe. A small change in friction can turn a stable grasp into a slip. Flexible or deformable items add further uncertainty because their shape changes as force is applied.
Vision can estimate position, but it does not directly reveal every contact force. Force sensors, joint-torque estimates, and tactile sensors can provide that missing information. The 2025 locomotion-and-manipulation survey emphasizes whole-body tactile sensing for contact-rich interaction and notes that force measurement and force estimation remain active challenges.
Generalization fails when the state distribution changes
A policy trained on demonstrations may encounter a different object pose, lighting condition, surface, or hand contact after deployment. This difference between training and deployment conditions is called covariate shift. The dexterous-manipulation survey identifies limited data and covariate shift as major obstacles: a controller can perform well on familiar states yet fail when a small variation puts it outside the training distribution.
Interactive imitation learning attempts to reduce this problem by allowing a person to correct the robot during execution. The survey considers interactive feedback promising, but says its use for dexterous manipulation is still limited. It is a developing way to gather corrective data, not proof that general dexterity has been solved.
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Bipedal balance leaves little room for error
A biped robot usually has a narrow support region compared with a four-legged machine. It must keep its center of mass and momentum compatible with the feet that are currently touching the ground. Reaching, carrying, pushing, or pulling changes those dynamics. A robot may be able to walk along a planned path but lose balance when it extends one arm, encounters a slippery patch, or receives an unexpected force.
Balance is also a recovery problem. A controller must detect a disturbance, estimate the robot’s state, and choose a corrective step, body motion, or contact before the available support is lost. Sensor delay and actuator limits reduce the time available for that response.
Locomotion and manipulation must be planned together
Many systems have advanced by treating walking and manipulation as separate capabilities. Real tasks often require both simultaneously. To open a heavy door, for example, a robot may need to place its feet, reach a handle, apply force, and shift its body while keeping enough support to resist the door. To pick an item from a low shelf, it may need to crouch, place a hand for support, and coordinate the arm with the changing leg posture.
Whole-body planners and controllers therefore select feasible contacts and motions for the feet, hands, and body together. They must account for disturbances and sensor updates without becoming too slow to run. The peer-reviewed 2026 survey in IEEE/ASME Transactions on Mechatronics describes a recurring trade-off: more detailed physical models can improve fidelity, but they also demand more computation. Simplified models run faster but may omit effects that matter during difficult contact.
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Perception, sensing, and the physical world
Seeing an object is not the same as knowing how to handle it
A camera can locate an object, but manipulation also depends on properties that are hard to infer visually: friction, compliance, mass distribution, whether an item is stuck, and how much force will damage it. The robot may need to make contact to learn those properties, turning perception into an active sensing problem.
Body motion further complicates perception. As the robot walks or turns, the camera view changes and the object may be briefly occluded by an arm or torso. A controller must combine visual, proprioceptive, force, and tactile information while keeping their coordinate frames aligned.
Contact planning is a separate capability
Humanoid tasks often depend on intentional contacts beyond the feet and grasping hand: a palm against a wall for support, a forearm against a surface, or a hand sliding along an edge. Planning those contacts requires geometry, force limits, friction assumptions, and a way to recover if the expected contact is absent. NIST’s proposed baseline benchmark specifically includes combined loco-manipulation and confined-space tasks because walking and hand motion alone do not measure this whole-body capability.
What learning methods can and cannot provide
No single control approach removes all of the trade-offs. Current surveys describe several complementary families:
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|---|---|---|
| Model-based planning and control | Uses a model of dynamics and contacts to enforce physical constraints and produce explainable motions. | Model errors, contact uncertainty, and computational cost can reduce performance in changing environments. |
| Reinforcement learning | Can discover behaviors through interaction or simulation and optimize a task objective. | Requires substantial data or simulation, can exploit unrealistic assumptions, and may generalize poorly. |
| Imitation learning | Uses demonstrations to avoid some trial-and-error and can learn complex action patterns. | Inherits gaps and biases in the demonstrations and is vulnerable to distribution shift. |
| Interactive imitation learning | Uses human corrections during execution to expose failures and improve behavior. | Feedback collection remains limited for high-dimensional dexterous tasks. |
| Foundation or language models | Can help with task interpretation, sequencing, and high-level decisions when connected to suitable robot policies. | Language output is not, by itself, a safe, real-time motor controller. |
The practical design is often a stack: a high-level system selects or describes a task, a motion planner generates feasible trajectories, and lower-level controllers regulate balance, force, and contact. A capable high-level model cannot compensate for missing tactile information, an unstable whole-body controller, or an interface that cannot express the required motor commands.
What the 2026 robot-control evidence actually shows
A July 9, 2026 report from Anthropic, Claude plays robotics, describes experiments in simulation and with physical embodiments, including a simulated humanoid, a robotic arm, and a real Unitree Go2 quadruped. The authors report that results depended strongly on the control interface. In that report they write, “Today’s frontier models cannot control humanoid robots without a pretrained policy.”
That statement applies to the models, interfaces, embodiments, and tests in the report. It should not be expanded into a claim about every robot controller or future model. Its broader lesson is that the boundary between a planning model and a robot’s low-level policy matters: the model needs an interface and a controller capable of turning decisions into stable, timed, force-aware actions.
Why demonstrations can look better than reliability
A demonstration establishes that one robot, controller, object, and environment produced an action at least once. It does not establish repeatability under different lighting, object placements, friction, disturbances, or task sequences. A staged scene may also remove navigation choices, unexpected obstacles, and the need to recover from mistakes.
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The proposed NIST benchmark
NIST says the 2013–2014 DARPA Robotics Challenge was the last time humanoid robot performance was rigorously measured between robots. Its proposed Humanoid Robot Baseline Performance Benchmark aims to provide low-footprint tests derived mostly from existing standardized methods. It is a proposal for capability measurement, not an already completed universal ranking.
The proposed categories span:
- mobility;
- manipulation and dexterity;
- combined loco-manipulation;
- confined-space whole-body awareness and control; and
- basic reasoning or scene understanding.
This structure reflects why “can it walk?” is an incomplete question. A useful comparison must test whether the robot can move while manipulating, maintain contact, operate in restricted spaces, understand a basic scene, and recover when conditions change.
Comparison criteria that prevent misleading claims
| Evaluation axis | Questions to ask |
|---|---|
| Task success and repeatability | Does it succeed repeatedly across changed object poses, lighting, surfaces, and disturbances? |
| Dexterity and force control | Can it adapt a grasp, regulate force, and prevent or recover from slips? |
| Balance and recovery | Can it remain stable or regain stability after a push, load change, or foot-placement error? |
| Whole-body coordination | Can feet, hands, torso, and supporting contacts work together during the task? |
| Sensing | Does it use tactile and contact-force information, or rely mainly on prepared visual conditions? |
| Efficiency and endurance | How much energy does locomotion consume, and how long can it operate before charging or thermal limits? |
| Generalization | Can it handle new objects, environments, and task sequences without retraining? |
| Human and environmental assistance | How much teleoperation, task-specific engineering, or workspace preparation is required? |
Mechanical specifications do not equal useful function
A robot’s number of joints, payload rating, walking speed, or ability to stand on one leg describes its structure or a controlled test. Those figures do not by themselves show dexterity or versatility. The 2023 comparative review Do robots outperform humans in human-centered domains? finds that some robot structures can outperform human structures on particular measures, while humanoid functions still lag human dexterity and versatility in complex tasks.
The same review identifies disadvantages in locomotion energy efficiency, operating time, and transportation costs. These system-level constraints matter in deployment: a robot that performs a short sequence in a laboratory may be impractical if it requires frequent charging, specialized transport, or constant supervision.
What “useful” means in the near term
There is no single industry-wide task-success percentage that represents humanoid robots. The reviewed sources use different embodiments, tasks, environments, and evaluation methods, and NIST’s proposed benchmark exists partly because consistent measurement is still needed.
Near-term usefulness is therefore more likely to appear in bounded settings where the object set, workspace, and task sequence can be specified. A robot may provide value when it can repeat a narrow operation with known fixtures, controlled safety boundaries, and a recovery procedure. That is different from a general household worker expected to improvise with arbitrary objects.
The strongest evidence for progress will be repeated, independently measured performance across the benchmark categories, with clear reporting of failures, intervention, energy use, and generalization. Until then, walking videos and single successful grasps should be treated as demonstrations of a capability, not proof of reliable real-world autonomy.
Further reading
For a technical treatment of the field, Humanoid Robotics: A Reference (2018) is cited by the 2025 locomotion-and-manipulation survey. It is intended for readers who want deeper coverage of humanoid mechanisms, control, planning, and interaction.
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