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}.

翻译：接触式任务因其固有的不确定性、复杂的动力学特性以及交互过程中的高损伤风险，对机器人系统提出了重大挑战。基于学习的控制方法的最新进展显示出使机器人在此类环境中获取并泛化复杂操作技能的巨大潜力，但确保探索与执行过程中的安全性，仍然是实现可靠实际部署的关键瓶颈。本综述全面概述了面向机器人接触式任务的安全学习方法。我们将现有方法分为两大领域：安全探索与安全执行。我们回顾了关键技术，包括约束强化学习、风险敏感优化、不确定性感知建模、控制屏障函数以及模型预测安全屏障，并重点阐述了这些方法如何整合先验知识、任务结构和在线适应以平衡安全与效率。本综述的一个特别重点在于，这些安全学习原则如何扩展至新兴的机器人基础模型（特别是视觉-语言模型和视觉-语言-动作模型）并与之交互，这些模型为接触式操作统一了感知、语言与控制。我们讨论了基于VLM/VLA方法所带来的新的安全机遇，例如约束的语言级规范和安全信号的多模态接地，以及它们所引入的放大风险和评估挑战。最后，我们概述了当前局限性与未来有前景的研究方向，旨在复杂接触式环境中部署可靠、安全对齐且具备基础模型能力的机器人。更多细节与材料请访问我们的\href{ https://github.com/jack-sherman01/Awesome-Learning4Safe-Contact-rich-tasks}{项目GitHub仓库}。