Abstract
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{ this https URL}{Project GitHub Repository}.
Abstract (translated)
接触丰富的任务对机器人系统提出了重大挑战，原因在于固有的不确定性、复杂的动力学以及互动过程中损坏的风险较高。最近基于学习的控制方法在使机器人获得并推广复杂操作技能方面展现了巨大潜力，但在探索和执行过程中的安全性保障仍是可靠实际部署的关键瓶颈。本次综述全面概述了用于机器人接触丰富任务的安全学习方法。我们将现有方法分为两个主要领域：安全探索与安全执行。我们回顾关键技术，包括约束强化学习、风险敏感优化、不确定性感知建模、控制屏障函数以及预测性安全保障盾，并强调这些方法如何结合先验知识、任务结构和在线适应来平衡安全性和效率。本次综述特别关注了这些安全学习原则如何扩展并应用于新兴的机器人基础模型中，尤其是视觉-语言模型（VLMs）和视觉-语言-动作模型（VLAs），它们统一了感知、语言和控制在接触丰富的操作中的应用。我们讨论了基于VLM/VLA方法的新安全性机会，例如以语言级规格化约束和多模态安全信号为特征的交互挑战以及由此带来的风险和评估难题。最后，我们概述了当前限制及未来向复杂接触丰富环境中部署可靠、与安全一致且具备基础模型能力的机器人发展的有前景的方向。 更多详情和材料请访问我们的 [项目GitHub仓库](https://github.com/your-project-repo)。
URL
https://arxiv.org/abs/2512.11908
