| title | Embedding Physics Priors in Robot Learning: A Survey |
|---|---|
| authors | Mattia Piccinini, Lucas Schulze, Alice Plebe, Matteo Saveriano, Thomas Beckers, Yuan Gao, Oleg Arenz, Baha Zarrouki, Dingrui Wang, Finn Rasmus Schäfer, Jan Peters, Johannes Betz, Gastone Pietro Rosati Papini |
| date | 2026-09-15 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.22319 |
| affiliation | TUM - Professorship of Autonomous Vehicle Systems |
| upvotes | 11 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | The rapid progress of artificial intelligence is reshaping robotics and accelerating the adoption of learning-based approaches. While purely data-driven methods have achieved remarkable success in computer vision and natural language processing, robotics remains constrained by limited data, complex real-world interactions, and the need for reliable operation. These challenges have motivated the exploration of physics-embedded robot learning, which embeds physics priors into learning algorithms. By encoding the underlying physical laws and constraints, physics priors can complement limited data with robotics-specific inductive biases, potentially improving generalization, interpretability, and sample efficiency. However, the literature on physics-embedded robot learning remains fragmented across terminology, methodologies, and application domains, making it difficult to assess this growing body of work. This survey reviews physics-embedded robot learning across a broad range of physics priors, robotics applications, and machine learning models, from single-layer perceptrons to generative foundation models. We adopt a unified taxonomy that classifies existing approaches according to their physics embedding: physics-guided inputs, data, and representations; physics-encoded model architectures; and physics-informed training loss functions. Building on this taxonomy, we review methods for robot dynamics learning, trajectory planning, prediction, control, and estimation, together with the corresponding open-source software ecosystem. We identify key open challenges, and outline promising future research directions. Overall, we argue that physics priors provide a particularly relevant robotics-specific inductive bias, complementing rather than replacing data-driven learning, and paving the way toward more generalizable, data-efficient, and trustworthy robotic systems. |
cite
@misc{piccinini2026embedding,
title={Embedding Physics Priors in Robot Learning: A Survey},
author={Mattia Piccinini and Lucas Schulze and Alice Plebe and Matteo Saveriano and Thomas Beckers and Yuan Gao and Oleg Arenz and Baha Zarrouki and Dingrui Wang and Finn Rasmus Schäfer and Jan Peters and Johannes Betz and Gastone Pietro Rosati Papini},
year={2026},
eprint={2609.22319},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.22319}
}