# 在机器人学习中嵌入物理先验：一项综述

> 原文链接：https://robotics.hellobike.com/papers/2026-09-15-2609-22319
> 频道：papers | 更新日期：2026-09-15 | 标签：arxiv, cs.RO
> 核心摘要：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。

# 在机器人学习中嵌入物理先验：一项综述

::: v-pre
> 原文链接：[Embedding Physics Priors in Robot Learning: A Survey](https://arxiv.org/abs/2609.22319)（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
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## 原始参考来源 (Sources)

- [arXiv 2609.22319：Embedding Physics Priors in Robot Learning: A Survey](https://arxiv.org/abs/2609.22319)