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Categories

The Awesome Physical AI list is organised into 14 canonical categories. Each category mirrors the corresponding section of the root README.md — the README remains the source of truth. Each category page now opens with a short engineering-oriented intro covering what it is, why it matters for deployment, and how to choose between entries.

  • Simulators — Physics engines and rendering stacks where policies are trained and stress-tested before touching hardware.
  • Datasets — Recorded robot behaviour — teleoperation, demonstrations, egocentric video — that feeds imitation learning and pretraining.
  • Benchmarks — Fixed task suites for comparing policies under controlled conditions across manipulation, locomotion, and embodied reasoning.
  • Evaluation Methodology — How to measure policies correctly — harnesses, metrics, and statistical practice that turn rollouts into defensible claims.
  • Robotics Foundation Models — Generalist pretrained policies and vision-language-action (VLA) models that map perception and language to robot actions.
  • World Models — Learned predictive models of physical dynamics used for imagination-based planning, sample-efficient RL, and synthetic data.
  • Manipulation — Grasping, dexterous, and contact-rich methods — analytic planners, diffusion and transformer policies, and the rigs that feed them.
  • Locomotion — Legged, bipedal, and humanoid controllers and the GPU-parallel training stacks that make modern sim-to-real practical.
  • Sim-to-Real — Techniques for closing the reality gap — domain randomisation, system identification, residual learning, and real-robot fine-tuning.
  • Safety & Robustness — Constrained training, adversarial evaluation, and failure-mode analysis that decide whether a system is shippable.
  • Governance & Policy — Standards, regulation, and risk frameworks that shape how Physical AI systems are evaluated and deployed.
  • Production Patterns / Reference Architectures — Middleware, planners, fleet orchestration, and observability for shipping robots in production.
  • Courses — Structured learning paths into robot learning and embodied AI for individuals and team onboarding.
  • Companies — Organisations actively shaping Physical AI in industry — foundation-model labs, platform builders, and applied deployers.