让机器人在执行任务时主动感知并应对突发状况。
Robot Planning and Situation Handling with Active Perception
- 用视觉语言模型主动选择视角，实时评估环境变化。
- 结合场景图实现任务与运动规划的统一推理。
- 适合需要长期自主的移动操作机器人系统。
当前机器人虽能规划复杂任务，但真实环境开放且动态，执行过程中常出现门卡住、地面有掉落物等未预见情况，可能源于自身动作失败或外部干扰（如人类活动）。及时检测与处理这些运行时状况仍是重大挑战，限制了机器人长期自主能力。本文提出名为 VAP-TAMP 的规划与情境处理框架，使机器人在任务执行中主动感知并应对突发情况。该框架利用动作知识，驱动视觉语言模型进行主动视点选择与情境评估，同时构建并推理场景图，实现任务与运动规划的融合。我们在仿真环境和服务任务中，以及在一台移动操作平台上对 VAP-TAMP 进行了评估。
原文摘要 · Abstract (English)
Current robots are capable of computing plans to accomplish complex tasks. However, real-world environments are inherently open and dynamic, and unforeseen situations frequently arise during plan execution, such as jamming doors and fallen objects on the floor. These situations may result from the robot's own action failures or from external disturbances, such as human activities. Detecting and handling such execution - time situations remains a significant challenge, limiting those robots' ability to achieve long-term autonomy. In this paper, we develop a planning and situation-handling framework, called VAP-TAMP, that enables robots to actively perceive and address unforeseen situations during plan execution. VAP-TAMP leverages action knowledge to strategically prompt vision-language models for active view selection and situation assessment, while constructing and reasoning over scene graphs for integrated task and motion planning. We evaluated VAP-TAMP using service tasks in simulation and on a mobile manipulation platform.
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。