Publikation
Embedding Physics Priors in Robot Learning: A Survey
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
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2609.22319, Pages 1-47, arXiv, 2026.
Zusammenfassung
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. All reviewed papers, classification and search methods, tables, and software
are available in a continuously updated public repository: https://github.com/TUM-AVS/survey-physics-embedded-
robot-learning.