[Submitted on 15 Sep 2026]
Title:Embedding Physics Priors in Robot Learning: A Survey
View a PDF of the paper titled Embedding Physics Priors in Robot Learning: A Survey, by Mattia Piccinini and 11 other authors
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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.
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2609.22319 [cs.RO]
(or arXiv:2609.22319v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.22319
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Mattia Piccinini [view email] [v1] Tue, 15 Sep 2026 16:53:06 UTC (4,492 KB)
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