外观
论文arXiv 2609.223191 个月前
在机器人学习中嵌入物理先验：一项综述
原文链接：Embedding Physics Priors in Robot Learning: A Survey（arXiv 2609.22319 · 发布日期：2026-09-15）
Embedding Physics Priors in Robot Learning: A Survey
本文针对物理嵌入机器人学习文献碎片化问题，提出统一分类并综述多类先验、应用、模型及开源生态。
核心要点
- 纯数据驱动方法在机器人中受限于数据有限、真实交互复杂和可靠运行需求。
- 物理先验通过编码物理规律与约束，补充有限数据并提供机器人归纳偏置。
- 综述按物理引导输入、数据与表征，物理编码架构和物理信息损失统一分类。
- 覆盖动力学学习、轨迹规划、预测、控制、估计及开源软件生态，并提出挑战与方向。
验证范围与局限：本文为综述，摘要未报告新的仿真或真机实验验证。
英文摘要 (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.
作者：Mattia Piccinini, Lucas Schulze, Alice Plebe, Matteo Saveriano, Thomas Beckers, Yuan Gao, Oleg Arenz, Baha Zarrouki, Dingrui Wang, Finn Rasmus Schäfer
来源
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