语义安全的机器人操作：从语义场景理解到运动安全防护
Semantically Safe Robot Manipulation: From Semantic Scene Understanding to Motion Safeguards
AI总结：
本文提出语义安全过滤器框架，结合三维语义建图与大语言模型推理识别语义不安全条件，并通过控制屏障认证将其映射为安全动作，在遥操作和真实厨房扩散策略实验中验证了超越传统碰撞避免的安全操作能力。
摘要：
在以人为中心的环境中确保安全交互，要求机器人理解并遵守被人类视为“常识”的约束（例如，“将一杯水举过笔记本电脑上方是不安全的，因为水可能洒出”或“旋转一杯水是不安全的，因为这可能导致其内容物倾倒”）。计算机视觉和机器学习的最新进展使机器人能够获得对其操作环境的语义理解并据此进行推理。尽管关于安全机器人决策的文献十分丰富，但语义理解很少被整合到这些形式化方法中。在本工作中，我们提出了一个语义安全过滤器框架，用于根据语义定义的约束（例如，不安全的空间关系、行为和姿态）和几何定义的约束（例如，环境碰撞和自碰撞约束）来认证机器人输入。在我们提出的方法中，给定感知输入，我们构建三维环境的语义地图，并利用大语言模型的上下文推理能力来推断语义不安全条件。然后，通过控制屏障认证形式化方法，将这些语义不安全条件映射到安全动作。我们在遥操作任务中展示了所提出的语义安全过滤器，并在真实世界厨房环境中应用了学习到的扩散策略，进一步展示了其在处理实际语义安全约束方面的有效性。这些实验共同突显了我们的方法将语义整合到安全认证中的能力，使机器人能够在传统碰撞避免之外实现安全操作。
英文摘要：
Ensuring safe interactions in human-centric environments requires robots to understand and adhere to constraints recognized by humans as "common sense" (e.g., "moving a cup of water above a laptop is unsafe as the water may spill" or "rotating a cup of water is unsafe as it can lead to pouring its content"). Recent advances in computer vision and machine learning have enabled robots to acquire a semantic understanding of and reason about their operating environments. While extensive literature on safe robot decision-making exists, semantic understanding is rarely integrated into these formulations. In this work, we propose a semantic safety filter framework to certify robot inputs with respect to semantically defined constraints (e.g., unsafe spatial relationships, behaviors, and poses) and geometrically defined constraints (e.g., environment-collision and self-collision constraints). In our proposed approach, given perception inputs, we build a semantic map of the 3D environment and leverage the contextual reasoning capabilities of large language models to infer semantically unsafe conditions. These semantically unsafe conditions are then mapped to safe actions through a control barrier certification formulation. We demonstrate the proposed semantic safety filter in teleoperated manipulation tasks and with learned diffusion policies applied in a real-world kitchen environment that further showcases its effectiveness in addressing practical semantic safety constraints. Together, these experiments highlight our approach's capability to integrate semantics into safety certification, enabling safe robot operation beyond traditional collision avoidance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
