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A humanoid robot works by repeatedly sensing its surroundings and its own condition, deciding what to do, and sending commands to mechanisms that move its body. Its AI does not act on its own: useful behavior depends on sensors, computing, control software, actuators, an energy supply, and the environment working together. Safety likewise depends on more than the robot’s software or body; in industrial settings, the way the robot is integrated into an application matters too.
The basic loop: sense, decide, move, and sense again
A humanoid robot is an embodied system, not just an AI model attached to a human-shaped shell. It has physical parts that move, sensors that report what is happening, computing hardware and software that interpret those reports, and an energy system that powers operation. Movement changes the robot’s position and the scene around it, so the next round of sensing has new information to work with.
ITU-T Recommendation F.748.66, approved on December 14, 2025, offers a framework for embodied AI organized around perception, decision-making, execution, interaction, and learning. It also describes physical components including perception sensors, computing, mechanical execution components and end effectors, drive mechanisms, and an energy system. These categories help explain how the pieces fit, but they are not a claim that every robot uses identical hardware or software architecture. ITU-T F.748.66
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- Interpret: Computing and perception software turn sensor readings into a useful estimate of what is happening.
- Choose: Decision-making software selects or adjusts an action in light of the goal and current state.
- Execute: Control commands drive joints or end effectors through the robot’s actuators and drive mechanisms.
- Update: New movement changes what sensors detect, allowing the robot to revise its state estimate and continue the loop.
The cycle is continuous in concept, even though real systems may divide its work among many software modules. Its practical importance is that an action is not finished when the robot issues a command: the robot must also account for what actually happened.
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How humanoid robot sensors work
Sensors are a family of inputs, not a synonym for cameras. F.748.66 identifies image, audio, tactile, and force information, and describes perception of spatial structure, relationships between objects, and potential changes in the environment. The recommendation does not prescribe a single bill of materials for every humanoid, so a particular platform should not be assumed to have any specific camera, lidar, inertial measurement unit, or force-sensor arrangement based on its shape alone.
Perceiving the environment
External sensing helps a robot build a representation of the scene relevant to its task: where objects are, how they relate to one another, and what may change. Image inputs can contribute visual information; audio inputs can provide sound information; tactile and force inputs can indicate contact or applied force. The framework names these signal types without requiring that each system implement them in the same way or combine all of them.
That distinction matters because sensing is not simply “recognize an object.” A task can depend on spatial relationships and change: whether an object is within reach, whether the scene has shifted, or whether contact has occurred. A robot that acts on an outdated or incomplete estimate may issue a command that no longer suits the situation. Perception therefore has to support action, not merely label items in an image.
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The robot also needs information about itself. The ITU framework includes physical and internal state such as position, velocity, acceleration, battery level, temperature, and computing resources. These readings help connect intended movement to actual movement: a command has different implications if a joint is moving differently than expected, the robot’s condition has changed, or the resources available to continue are constrained.
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External and internal sensing answer different questions. Environmental perception concerns what is around the robot; body-state perception concerns what the robot is doing and what condition it is in. A useful estimate must bring the task and the robot’s present state together. A high-level instruction cannot by itself tell the system whether a planned movement is currently feasible.
How AI turns a goal into movement
The decision layer receives information from perception, considers the task and current state, and chooses or refines actions. F.748.66 describes reasoning about tasks and states, decomposing goals into action steps, and generating control commands directly or through other control modules. It also calls for dynamic optimization and real-time adjustment. This is a framework for system capabilities, not a guarantee that today’s humanoids can reliably perform arbitrary household or industrial work.
Task planning and motion control are different jobs
Consider the instruction “move an object to a location.” At the task-planning level, the system needs to turn that goal into a sequence of actions. At the control level, those actions must become coordinated commands that produce stable locomotion or manipulation. The two layers are related, but they solve different problems: choosing a meaningful next step is not the same as making the body carry it out safely and accurately.
This separation helps explain why a convincing demonstration of one behavior is not evidence that a robot can perform every related task. The task planner must respond to the actual scene, while the control system must produce the intended movement under the robot’s current conditions. If the scene or the robot’s state changes, the system needs to update its estimate and adjust its actions rather than blindly continue an obsolete sequence.
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Why feedback matters
A command is a request to the physical system, not proof of the result. Feedback from sensors gives the robot information it can use to compare its intended action with what is happening. That is why perception, decision-making, and execution belong in a loop: if new readings show a different state or changed scene, software can revise the next command. The framework’s call for real-time adjustment describes a capability goal; it does not establish that all robots react equally quickly or effectively.
How actuators, drive mechanisms, and energy create motion
Actuators are on the output side of the loop. They turn control commands into movement at joints or end effectors—the parts that interact with objects or the environment. F.748.66 groups mechanical parts, end effectors, and drive mechanisms within the execution system, and names motors, hydraulics, and pneumatics as possible drive technologies.
The source identifies these as possible technologies, not a ranking or a specification for a typical humanoid. The exact choice depends on the design. In any case, the robot has to produce movement that is both controlled and strong enough for its intended work while fitting within constraints on size, weight, energy, and heat. These are linked engineering trade-offs: a movement mechanism, its power needs, and the body carrying it cannot be considered independently.
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How humanoid robots stay safe around people
Safety is a property of the robot and the application in which it operates. The robot’s sensing and control behavior matter, but so do the surrounding system, procedures, and integration. A safety claim should therefore be tied to the intended task and setting rather than inferred from a human-like appearance, a general AI capability, or a standard’s title.
Industrial robot requirements and integration
For industrial use, ISO separates requirements for the robot itself from requirements for integrating robots into complete systems and applications. ISO 10218-1:2025 covers industrial robots as machines; ISO 10218-2:2025 covers integration and application requirements. The distinction matters because a robot installed in a working application interacts with more than its own hardware: its surrounding equipment and the way the system is set up are part of the safety picture. ISO 10218-1:2025 and ISO 10218-2:2025
These standards should not be read as proof that every humanoid is certified, nor as a blanket rule set for every home, public-space, or general-purpose use. Their cited scope is industrial robots and industrial integration. Whether a particular system is appropriate for a particular use requires evidence about that system and its application, not an assumption based on the existence of a standard.
Measuring perception, dexterity, and force behavior
Safety depends in part on whether a system can perceive and respond to relevant conditions. NIST’s Measurement Science for Robotics and Autonomous Systems Program describes measurement work on perception performance and on human-like dexterity and force-control characteristics relevant to safe collaboration. Measurement is important because broad labels such as “perceptive” or “collaborative” do not, by themselves, tell an operator how the robot behaves in a specific task. NIST Measurement Science for Robotics and Autonomous Systems Program
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In practical terms, a safety evaluation needs to consider what the robot can sense, how it behaves when interacting with people or objects, and how the application is organized. The cited NIST work supports the importance of measurable perception, dexterity, and force control; it does not establish a single performance threshold or certify a given humanoid for a particular deployment.
Standards frameworks are not capability guarantees
ITU-T F.748.66 provides a framework for embodied AI system requirements and capabilities. A separate project, IEEE P3870, is active standards work. Its scope includes a reference framework, general requirements, and safety assurance for intelligent control systems in humanoid robots used in digital production and services; the IEEE page lists PAR approval on September 25, 2026. Because it is a project rather than a completed published standard on that page, it should not be treated as an already applicable finished requirement. IEEE P3870 project page
More generally, a framework or standards project describes how requirements and evaluation may be organized. It does not, on its own, prove that a particular robot has met them, can perform a task reliably, or is safe in every environment. That conclusion must come from evidence about the robot and the actual application.
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Product-by-product specifications are not established by the framework and standards sources above, so they do not support ranking named robots or claiming that one model is safer or more capable than another. For a real evaluation, ask for evidence tied to the intended work rather than relying on category-wide assumptions.
- Sensing: Which external and internal signal types does the system support, and what information does it use to estimate its state?
- Movement: What evidence demonstrates the locomotion or manipulation required for the particular task?
- Changing conditions: Can the system respond when the scene or its own state differs from what it expected?
- Operating constraints: What are the documented energy and operating limits for the specific model and application?
- Human interaction: What evidence describes its behavior and force control when working near people?
- Application safety: How are the robot, surrounding equipment, procedures, and integration addressed for the intended industrial use?
These questions turn “Is this humanoid safe and capable?” into a more answerable inquiry: safe and capable of doing what, under which conditions, and with what supporting evidence? The cited sources establish system-level concepts and relevant industrial safety distinctions; they do not publish the model-specific measurements needed to answer those questions for an individual product.
Why the whole system matters
A humanoid robot’s behavior emerges from the interaction of sensing, software, movement mechanisms, energy, and environment. Sensors must supply useful information about both the scene and the body; AI and control must turn goals into actions and adjust to feedback; actuators must make those commands physical; and the system must operate within its design and application constraints. Safety is not an isolated AI feature: in industrial settings it includes both robot-level requirements and integration into the complete application.
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