The Anatomy of Humanoid Robots
This section explores the physical embodiment and morphological design of advanced robotic systems. Unlike traditional software-based AI, embodied intelligence introduces unique physical and psychological dimensions to human-machine interaction. To properly frame the legal and technical problem, it is essential to distinguish between the core technological layers.
The Eyes, Ears, and Skin of the Machine
Through an advanced perception stack, comprising 360-degree cameras, LiDAR sensors, ambient microphones, and tactile receptors embedded in synthetic skin, Humanoid Robots do not merely observe their environment; they continuously harvest intimate environmental, behavioral, and psychological data. Disguised by an approachable anthropomorphic form, this relentless sensory apparatus blurs the boundaries of domestic privacy, laying the groundwork for unprecedented cognitive data collection.
Explore Taxanomy
Here is a brief academic description of humanoid robot technology, integrating the required definitions from the Artificial Intelligence Act, ISO standards, and the International Telecommunication Union:
The technology underlying humanoid robots represents a sophisticated convergence of physical embodiment and artificial intelligence, designed to navigate, interact with, and adapt to human-centric environments. From a regulatory and technical standpoint, defining these systems requires examining multiple institutional frameworks. Specifically, this research navigates the legal definitions of artificial intelligence established by the European Artificial Intelligence Act, integrates technical parameters from ISO standards dedicated to robotics and artificial intelligence, and incorporates foundational insights from the International Telecommunication Union regarding the evolving scope of Embodied AI.
Legal Reference
Article 3 (Definitions) (1) Regulation (EU) 2024/1689 (Artificial Intelligence Act)
Definition
‘AI system’ means a machine-based system that is designed to operate with varying levels of autonomy and that may exhibit adaptiveness after deployment, and that, for explicit or implicit objectives, infers, from the input it receives, how to generate outputs such as predictions, content, recommendations, or decisions that can influence physical or virtual environments;
Artificial intelligence Vocabulary Standard
Information technology | Artificial intelligence — Vocabulary
| ISO/IEC 22989:2022 | Definition | |
|---|---|---|
| Al system | ISO/IEC 22989:2022 (3.1.4) | engineered system that generates outputs such as content, forecasts, recommendations or decisions for a given set of human-defined objectives |
| Autonomy | ISO/IEC 22989:2022 (3.1.5) | characteristic of a system that is capable of modifying its intended domain of use or goal without external intervention, control or oversight |
| Automatic - Automation - Automated | ISO/IEC 22989:2022 (3.1.7) | pertaining to a process or system that, under specified conditions, functions without human intervention |
| Cognitive computing | ISO/IEC 22989:2022 (3.1.8) | category of Al systems (3.1.4) that enables people and machines to interact more naturally. |
| Note 1 to entry: Cognitive computing tasks are associated with machine learning (3.3.5), speech processing, natural language processing (3.6.9), computer vision (3.7.1) and human-machine interfaces. | ||
| Data mining | ISO/IEC 22989:2022 (3.1.11) | computational process that extracts patterns by analysing quantitative data from different perspectives and dimensions, categorizing them, and summarizing potential relationships and impacts |
| Robot | ISO/IEC 22989:2022 (3.1.29) | automation system with actuators that performs intended tasks (3.1.35) in the physical world, by means of sensing its environment and a software control system |
| Note 1 to entry: A robot includes the control system and interface of a control system. Note 2 to entry: The classification of a robot as industrial robot or service robot is done according to its intended application. Note 3 to entry: In order to properly perform its tasks (3.1.35), a robot makes use of different kinds of sensors to confirm its current state and perceive the elements composing the environment in which it operates. | ||
| Natural language | ISO/IEC 22989:2022 (3.6.7) | language that is or was in active use in a community of people and whose rules are deduced from usage |
| Note 1 to entry: Natural language is any human language, which can be expressed in text, speech, sign language, etc. Note 2 to entry: Natural language is any human language, such as English, Spanish, Arabic, Chinese or Japanese, to be distinguished from programming and formal languages, such as Java, Fortran, C++ or First-Order Logic. | ||
| Dialogue management | ISO/IEC 22989:2022 (3.6.2) | task (3.1.35) of choosing the appropriate next move in a dialogue based on user input, the dialogue history and other contextual knowledge (3.1.21), to meet a desired goal |
| Emotion recognition | ISO/IEC 22989:2022 (3.6.3) | task (3.1.35) of computationally identifying and categorizing emotions expressed in a piece of text, speech, video or image or combination thereof. |
| Sentiment analysis | ISO/IEC 22989:2022 (3.6.16) | task (3.1.35) of computationally identifying and categorizing opinions expressed in a piece of text, speech or image, to determine a range of feeling such as from positive to negative. |
| Note 1 to entry: Examples of sentiments include approval, disapproval, positive toward, negative toward, agreement and disagreement. | ||
| Face recognition | ISO/IEC 22989:2022 (3.7.2) | automatic pattern recognition comparing stored images of human faces with the image of an actual face, indicating any matching, if it exists, and any data, if they exist, identifying the person to whom the face belongs |
Embodied AI (EAI) | International Telecommunication Union (ITU)
Recommendation ITU-T F.748.66 (12/2025)
Requirements and framework for embodied artificial intelligence systems.
| Embodied Artificial Intelligence (EAI) | Details |
|---|---|
| Definition (3.2.1) | Artificial intelligence integrated into physical systems that interact autonomously with and adapt to the physical world. |
| Description (6.1) | Embodied AI (EAI) is an advanced and emerging branch of artificial intelligence [...]. It extends artificial intelligence into the physical world, further reducing human cognitive burden in physical tasks, and enables systems to improve through direct data collection and interaction with the environment. |
| Human-machine interaction (8.4.1) [in Interaction capability (8.4)] | The system is required to support an advanced level of human-machine interaction, including one or more of the natural language processing, gesture recognition, facial expression detection, and body posture analysis. |
Features of EAI
Embodied AI (EAI) systems are characterized by three core features, where an ideal system in the strict sense possesses all three, whereas a system in the broad sense qualifies by exhibiting at least one of these traits.
- The first is a cognitive capability to construct an internal model of the world via physical perception, decision-making, and human-machine interaction.
- The second is a collaborative capability that enables interaction and cooperation with other embodied systems alongside operation within a cloud-edge-device collaborative architecture.
- The third is a learning capability that allows the system to continuously learn, improve, acquire new skills, and adapt to environmental changes over time
Framework of the EAI system
The general framework of the EAI system can be divided into three layers, including the basic layer, functional layer, and application layer:
The basic layer
The basic layer of the embodied AI system is structured around three core pillars.
First, foundation models, including vision foundation models, vision-language models, and vision-language-action models, provide the cognitive foundation for semantic understanding, reasoning, and environmental interaction. Second, the computing platform integrates cloud, edge, and on-premises environments to ensure low-latency, secure, and scalable execution of perception and control pipelines. Third, the physical body serves as the hardware carrier for the system, spanning humanoid robots, quadrupeds, and other platforms, and integrates a perception system for situational awareness, a dual computing unit, an execution system that translates instructions into actions, a drive system powered by motors, hydraulics, or pneumatics for precision, and an energy system utilizing high-density batteries for continuous power.
The functional layer
The functional layer of the embodied AI system is organized into five core modules.
First, the embodied perception module handles multi-modal sensory fusion, environment mapping, and dynamic tracking to establish situational awareness. Second, the embodied decision-making module processes perception data through task planning and decision optimization to generate step-by-step action instructions. Third, the embodied execution module carries out these commands via locomotion across various terrains and precise manipulation for object interaction. Fourth, the embodied interaction module facilitates human-machine collaboration and multi-machine coordination by utilizing action outcome feedback. Finally, the embodied learning module updates internal models and skills through environmental interaction, imitation, autonomous exploration, and self-reflection.
The application layer
The application layer operates above the functionality layers to provide service interfaces and deliver user- or task-oriented services, successfully translating system capabilities into applications for domain-specific scenarios.
Robotics Vocabulary Standard
Robotics — Vocabulary
| ISO 8373:2021 | Definition | |
|---|---|---|
| Robot | ISO 8373:2021 (3.1) | "programmed actuated mechanism with a degree of autonomy (3.2) to perform locomotion, manipulation or positioning" |
| Autonomy | ISO 8373:2021 (3.2) | "ability to perform intended tasks based on current state and sensing, without human intervention" |
| Service Robot | ISO 8373:2021 (3.7) | |
| Humanoid Robot | ISO 8373:2021 (4.15.5) | “a robot (3.1) with body, head and limbs, looking and moving like a human”. |
Scenario Analysis
Domestic Services and Personalized Assistance
Drawing upon the practical example provided in Appendix II.5 of the International Telecommunication Union Recommendation ITU-T F.748.66, embodied AI systems deployed in domestic environments are designed to assist with various household chores while offering companionship and dedicated support to individuals. Within this operational scenario, the system's architecture integrates multi-modal perception to process environmental context, human presence, and surrounding objects.
This perceptual data feeds into a reasoning engine that informs decision-making regarding necessary assistance tasks, which are subsequently carried out by the execution module through physical chores such as retrieving objects, cleaning surfaces, maintaining lawns, or preparing food. Furthermore, these systems facilitate user communication via speech, gestures, or touch interfaces, while leveraging continuous learning from user interactions and historical experiences to dynamically personalize their behavior, decision support, and task execution over time.
© 2026 Ester Sofia Fuligni
[Draft] Research Proposal | LL.M. Law & Technology | Track Privacy and Security | Tilburg Law School
Tilburg University Warandelaan 2, 5037 AB Tilburg, The Netherlands
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