HumanoidGPT · Part of The Humanoid Group

About HumanoidGPT

HumanoidGPT explains the AI behind humanoid robots, from foundation models and vision-language-action systems to training data and evaluation, in plain terms. It is an editorial project of The Humanoid Group.

Sources we rely on

AI Risk Management Framework — National Institute of Standards and Technology (NIST).
Voluntary US framework for mapping, measuring and managing AI risks, with a playbook and companion profiles.
ISO/IEC 22989:2022 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology — International Organization for Standardization (ISO) and IEC.
Agreed international terms and concepts for AI, useful for reading model claims in a common vocabulary.
ISO/IEC 23894:2023 Information technology — Artificial intelligence — Guidance on risk management — International Organization for Standardization (ISO) and IEC.
Guidance on folding AI-specific risks into an organisation's risk management when building, deploying or using AI products.
ISO/IEC 25059:2023 Software engineering — SQuaRE — Quality model for AI systems — International Organization for Standardization (ISO) and IEC.
Defines quality characteristics for specifying, measuring and evaluating AI systems, extending the SQuaRE software series.
The 2026 AI Index Report — Stanford Institute for Human-Centered Artificial Intelligence (HAI).
Annual data on AI research, technical performance, economics and policy, with public datasets behind each chapter.
On the Opportunities and Risks of Foundation Models — Stanford Center for Research on Foundation Models (arXiv).
The Stanford report that introduced the term foundation model, covering capabilities, training methods and societal risks.
Open X-Embodiment: Robotic Learning Datasets and RT-X Models — Open X-Embodiment Collaboration (arXiv).
Pools data from 22 robot types across 21 institutions and shows one model can carry skills between different robots.

Robot Foundation Models — Large models trained on broad data, then adapted to control robots across many tasks.
Vision-Language-Action Models — Models that see a scene, read an instruction and output the robot's next movements.
Data & Simulation — Teleoperation, demonstrations and simulated worlds: where robot training data comes from.
Evaluating Humanoid AI — How to judge a humanoid's intelligence on your own tasks, not in a demo video.
LLMs as Task Planners — How large language models break a spoken request into steps a robot can actually carry out.
World Models for Robots — Learned simulators and video prediction that let robots imagine the result of an action before they act.
On-Device or Cloud AI — Where a robot's AI runs, and how latency, privacy, connectivity and power shape that choice.
Open-Source Robot Models — Open robot foundation models and datasets: what gets released, what open really means and how to use them.
AI Regulation for Robots — How the EU AI Act, NIST's AI Risk Management Framework and India's AI guidelines apply to robots.
Latest updates — Robot models are now tested in unseen homes and judged on safety, yet benchmarks still show wide gaps. These figures and dated updates show what progressed, what failed and how to read the claims.
About us — What we cover, how we source every figure, and who publishes this site.

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