TUM survey unifies physics-embedded robot learning with a new taxonomy
TUM-AVS · hf · 2026-09-23
TUM-AVS released a survey, "Embedding Physics Priors in Robot Learning," arguing that physics priors serve as robotics-specific inductive biases that complement rather than replace data-driven learning, addressing limited data, complex real-world interactions and reliability requirements.
- Unified taxonomy: physics-guided inputs/data/representations, physics-encoded model architectures, and physics-informed training losses.
- Covers robot dynamics learning, trajectory planning, prediction, control and estimation, from perceptrons to generative foundation models.
- Reviews the open-source software ecosystem and outlines open challenges toward more generalizable, data-efficient and trustworthy robotic systems.
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