综述机器人高接触任务中的安全学习方法，涵盖从传统算法到视觉语言模型的新进展。
Safe Learning for Contact-Rich Robot Tasks: A Survey from Classical Learning-Based Methods to Safe Foundation Models
- 按安全探索与执行分类，梳理约束强化学习、不确定性建模等关键技术
- 提出安全与效率平衡的机制，支持在线适应与先验知识融合
- 重点分析视觉语言模型带来的新安全机会与风险挑战，适合研究者参考
高接触任务因内在不确定性、复杂动态和交互中高损伤风险，对机器人系统构成重大挑战。基于学习的控制虽在复杂操作技能获取与泛化上展现潜力，但探索与执行阶段的安全保障仍是真实部署的关键瓶颈。本综述系统梳理了面向机器人高接触任务的安全学习方法，将其分为安全探索与安全执行两大领域。回顾了约束强化学习、风险敏感优化、不确定性感知建模、控制屏障函数及模型预测安全罩等核心技术，阐明其如何融合先验知识、任务结构与在线适应以兼顾安全与效率。特别关注安全学习原则如何延伸至新兴的机器人基础模型，尤其是视觉-语言模型（VLM）与视觉-语言-动作模型（VLA），这些模型统一感知、语言与控制，适用于高接触操作。讨论了基于VLM/VLA的方法带来的新安全机遇，如语言级约束定义与多模态安全信号对齐，也指出其引发的放大风险与评估挑战。最后总结当前局限，并展望未来可靠、安全对齐且基于基础模型的机器人在复杂高接触环境中的部署方向。更多细节与资源见项目主页。
原文摘要 · Abstract (English)
Contact-rich tasks pose significant challenges for robotic systems due to inherent uncertainty, complex dynamics, and the high risk of damage during interaction. Recent advances in learning-based control have shown great potential in enabling robots to acquire and generalize complex manipulation skills in such environments, but ensuring safety, both during exploration and execution, remains a critical bottleneck for reliable real-world deployment. This survey provides a comprehensive overview of safe learning-based methods for robot contact-rich tasks. We categorize existing approaches into two main domains: safe exploration and safe execution. We review key techniques, including constrained reinforcement learning, risk-sensitive optimization, uncertainty-aware modeling, control barrier functions, and model predictive safety shields, and highlight how these methods incorporate prior knowledge, task structure, and online adaptation to balance safety and efficiency. A particular emphasis of this survey is on how these safe learning principles extend to and interact with emerging robotic foundation models, especially vision-language models (VLMs) and vision-language-action models (VLAs), which unify perception, language, and control for contact-rich manipulation. We discuss both the new safety opportunities enabled by VLM/VLA-based methods, such as language-level specification of constraints and multimodal grounding of safety signals, and the amplified risks and evaluation challenges they introduce. Finally, we outline current limitations and promising future directions toward deploying reliable, safety-aligned, and foundation-model-enabled robots in complex contact-rich environments. More details and materials are available at our \href{ https://github.com/jack-sherman01/Awesome-Learning4Safe-Contact-rich-tasks}{Project GitHub Repository}.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。