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Embedding Physics Priors in Robot Learning: A Survey — arXiv:2609.22319

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.22319
License (as archived): CC0-1.0
Archived: 2026-10-09T22:21:17.527Z
SHA-256 of the archived text: 3a2bd2bad1b85969d5a84ab450ef61e8251a841c25236d73e850e4a2b84ee41d
Stable URL of this page: https://metraipolis.com/documento/dc_277ccbb97fdf

How to cite

Embedding Physics Priors in Robot Learning: A Survey — arXiv:2609.22319 — arXiv (en), https://arxiv.org/abs/2609.22319 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_277ccbb97fdf (Metraipolis archive dc_277ccbb97fdf, sha256 3a2bd2bad1b85969d5a84ab450ef61e8251a841c25236d73e850e4a2b84ee41d)

BibTeX

@misc{dc277ccbb97fdf,
  title = {Embedding Physics Priors in Robot Learning: A Survey — arXiv:2609.22319},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.22319},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_277ccbb97fdf; sha256 3a2bd2bad1b85969d5a84ab450ef61e8251a841c25236d73e850e4a2b84ee41d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.22319. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-09-22 in cs.RO).
Categorie arXiv: Robotics (cs.RO).
Pagina del lavoro: https://arxiv.org/abs/2609.22319 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-09T21:59:59.416Z dalla pagina https://arxiv.org/catchup/cs.RO/2026-09-22?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).