Embedding Physics Priors in Robot Learning: A Survey
A survey reviews physics-embedded robot learning, proposing a unified taxonomy and arguing for its benefits over purely data-driven methods.
This survey reviews the field of physics-embedded robot learning, which integrates physics priors into learning algorithms to address challenges in robotics like limited data and the need for reliable operation. The authors propose a unified taxonomy that classifies approaches according to their physics embedding: physics-guided inputs, data, and representations; physics-encoded model architectures; and physics-informed training loss functions. The paper argues that physics priors provide a relevant robotics-specific inductive bias that complements data-driven learning, paving the way for more generalizable, data-efficient, and trustworthy robotic systems.
- Survey reviews physics-embedded robot learning across a broad range of applications.
- A unified taxonomy classifies approaches by how physics is embedded (inputs, architectures, loss functions).
- Physics priors are argued to complement data-driven learning for more generalizable robotic systems.
- The survey covers methods from simple models to generative foundation models.
From the original article
1 min read at huggingface.co
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.
By Mattia Piccinini, Lucas Schulze, Alice Plebe · huggingface.co
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