A humanoid robot works through a continuous exchange between its body and its software. Sensors measure the environment and the robot’s own condition; computing systems interpret those signals and plan actions; control software turns plans into commands; and actuators move the robot. The movement changes what the sensors see, starting the cycle again.
That cycle explains both what AI can contribute and why it cannot be considered separately from the robot’s mechanics, energy supply, and operating environment. A task plan is not proof that a robot can carry out the task reliably—or safely—in every setting.
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How does a humanoid robot turn information into movement?
A useful way to understand a humanoid robot is as an embodied system rather than a computer with a human-shaped shell. Its capabilities depend on several connected parts: sensing hardware, computing, decision-making and control software, mechanical execution components, drive mechanisms, end effectors, and an energy system. The International Telecommunication Union (ITU) uses a similar set of capability areas—perception, decision-making, execution, interaction, and learning—in Recommendation F.748.66, approved on December 14, 2025. The recommendation describes a framework for embodied AI systems, not a single required robot design.
- Sense: Gather information about nearby objects and space, along with the robot’s own physical and operating state.
- Interpret: Use software to form a working estimate of what is happening and what the robot can currently do.
- Decide: Select or refine actions in light of the task and the current estimate.
- Execute: Send commands through control software to drives and actuators that produce movement.
- Observe again: Measure the result and update the estimate as the robot and its surroundings change.
This loop is a system-level explanation, not a claim that all humanoids use identical software modules or run one uniform AI model. The important principle is that movement feeds new information back into the system: a hand’s position changes, an object may shift, and the robot’s balance or energy state may change. A controller that acts without accounting for those effects would be working from an increasingly outdated picture.
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How do humanoid robot sensors work?
“Sensors” means a collection of ways to measure the world and the robot—not simply a camera. The ITU framework identifies image, audio, tactile, and force inputs. These can help a system detect spatial structure, relationships between objects, and changes that may matter to a task. The exact equipment varies by platform and application; the framework does not prescribe one universal sensor bill of materials for humanoids.
Sensing the environment
Environmental perception helps a robot build a representation of its surroundings: where relevant objects are, how they relate to one another, and what may be changing. Image input can contribute visual information, audio can provide sound information, and tactile or force input can provide information associated with contact. These are categories of signals, not a guarantee that a particular robot has every modality or can interpret it equally well in every condition.
The distinction matters because a task often depends on more than recognizing an object. To reach for something, a robot needs information useful for locating it relative to itself and for judging whether the scene has changed. To interact through contact, information about touch or force may be relevant as well. The framework describes these perception needs at a capability level; it does not establish a specific camera, lidar, inertial measurement unit, or force-sensor configuration for every machine.
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Perception also includes the robot’s internal physical and operating state. The ITU framework names position, velocity, acceleration, battery level, temperature, and computing resources. This body-state information helps the system compare intended action with actual condition. A command to move a limb is only useful if the robot can estimate what that limb is doing; decisions may also need to reflect limits such as available energy, heat, or computing capacity.
External and internal sensing therefore serve different but connected purposes. One helps describe the task environment; the other helps describe the machine that must act in it. The resulting estimates are inputs to later decisions, not perfect or complete knowledge of the world.
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How does AI control a humanoid robot?
The decision layer uses perception information and the task goal to choose actions. In the ITU framework, decision-making includes reasoning about the task and current state, decomposing a goal into action steps, generating control commands directly or through other control modules, and dynamically optimizing or adjusting decisions in real time. In ordinary terms, software must work out not only what outcome is wanted, but what the robot should try next given what it currently senses.
Planning a task is different from controlling a movement
Consider the instruction “move an object to a location.” That is a high-level goal. A planner may need to turn it into intermediate actions such as approaching, reaching, making contact, moving the object, and releasing it. The goal describes the intended result; it does not specify every joint movement needed to achieve it.
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Below that planning level, control has to produce coordinated movement that remains stable and responds to the robot’s current state. A suitable action sequence still has to be translated into motions that the body can execute. The distinction between planning and control is useful even when an implementation combines functions or uses different internal architecture: choosing an action and physically carrying it out are related but separate problems.
What AI does—and does not—establish
AI can contribute to interpreting information, reasoning about a task, selecting steps, and adapting decisions as conditions change. Its output is constrained by the sensors, computation, energy, mechanics, and control methods available to the physical robot. The ITU recommendation describes capabilities and a system framework; it is not evidence that today’s humanoids can reliably perform arbitrary household or industrial tasks. A robot’s stated or demonstrated ability to plan a task should not be treated as proof that it can complete that task robustly in a different environment.
What do actuators, drives, and end effectors do?
Actuators are part of the execution side of the system: they turn commands into physical motion. Through the robot’s mechanical structure, motion can occur at joints or at end effectors—the components that interact with objects or the environment. The ITU framework groups mechanical parts, end effectors, and drive mechanisms within execution. It names motors, hydraulics, and pneumatics as possible drive technologies; it does not say that every humanoid uses all of them or specify a single best choice.
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The practical engineering challenge is to make movement strong and controlled while keeping the system within its size, weight, energy, and thermal limits. Those considerations are connected. A body has to carry its mechanisms and energy supply while producing the movement its task requires; operating those mechanisms also has consequences for energy use and temperature. The available evidence here does not establish comparable performance specifications for particular commercial robots, so it cannot support a product ranking or a general claim about which drive technology is superior.
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How does a humanoid robot get and manage energy?
The energy system supplies the robot’s body and is part of the embodied system, not an accessory to its intelligence. The ITU recommendation describes an energy system and identifies high-density batteries as a common means of supplying power. It does not provide a universal battery capacity, operating duration, recharge time, or duty cycle for humanoids. Those figures would depend on the specific robot and conditions, and cannot be inferred from the framework.
Energy and thermal conditions can shape what the robot is able to do. The framework’s inclusion of battery level and temperature among internal-state information means these are relevant signals for perception and decision-making. At a general level, the system has to account for its available resources while choosing and executing actions. It would be misleading to promise a particular runtime or workload without model-specific evidence and the conditions under which that figure was measured.
How do humanoid robots stay safe around people?
Safety is a property of the robot and the situation in which it is used. Sensing and control can contribute to safer behavior, but safe operation also depends on the machine’s design, its integration into a complete application, and the procedures and conditions surrounding that application. A robot that is suitable for one controlled industrial setup is not automatically established as safe for a home, public space, or every other use.
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Industrial robot requirements and application integration
For industrial applications, it is important to distinguish requirements for the robot itself from requirements for the completed system. ISO 10218-1:2025 addresses safety requirements for industrial robots as machines. ISO says that integration and application requirements are covered by ISO 10218-2:2025. The ISO 10218-1:2025 page identifies that division of coverage. A robot-level standard and integration-level requirements address different parts of the safety picture; meeting or referencing one does not, by itself, establish that every deployment is safe.
These industrial standards should not be presented as a blanket certification of every humanoid robot or as a complete rule set for homes, public spaces, or all-purpose robots. The intended use and complete application matter. A safety assessment needs to consider the robot and its integration for that use rather than extrapolating from a general humanoid label.
Why measurement matters
Safety claims are more useful when they can be evaluated through relevant measurements. The U.S. National Institute of Standards and Technology (NIST) describes measurement work for robotics and autonomous systems that includes perception performance, human-like dexterity, and force-control characteristics relevant to safe collaboration. NIST’s program overview explains this measurement-science focus. Such work helps clarify what needs to be measured; it is not a blanket finding that any particular humanoid is safe in any setting.
For a specific application, the practical question is whether the robot’s sensing and force behavior, its control, and the way it has been integrated have been evaluated for the task and surroundings in question. A capability framework or standards reference can describe requirements or evaluation needs, but neither alone proves reliable performance or safety in every environment.
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ITU-T F.748.66 provides a current framework for embodied AI capabilities and the physical system that supports them. It is useful for understanding how perception, decision-making, execution, interaction, and learning fit together, but it should not be mistaken for proof of a particular robot’s performance.
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IEEE P3870 is a separate, active standards project, not a completed published standard. Its scope covers a reference framework, general requirements, and safety assurance for intelligent control systems in humanoid robots used in digital production and services. The IEEE project page lists PAR approval on September 25, 2026. The project page describes its scope and status. Because the work is active, it should not be described as an already applicable finished requirement.
The ITU also provides an overview of embodied AI. Taken together, these materials help frame the system and the questions standards work addresses. They do not substitute for evidence about a particular model or the application in which it will operate.
How should you judge a humanoid robot’s capabilities?
Claims about a humanoid become easier to assess when separated into evidence about the whole operating system. For the task and setting you care about, look for information in these areas:
- Sensing: Which environmental signal types and internal-state measurements are supported, and what evidence shows how they perform in the intended conditions?
- Task and movement: What locomotion and manipulation capabilities are demonstrated, and how does the system respond when the scene changes?
- Resources: What energy and operating constraints are documented for the specific robot and use, rather than inferred from a general description?
- Interaction: What kind of interaction with people or the environment is intended, and what limits are stated?
- Safety evidence: What has been evaluated for the robot itself and for its integration into the specific application?
Without model-specific evidence, these questions are a way to distinguish a system-level description from a substantiated capability claim—not a basis for ranking products. The central test is whether the entire loop, from sensing through execution and back to feedback, has evidence relevant to the task and environment being claimed.
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