发表机构
Hangzhou Dianzi University; Zhejiang University; New York University; Italian Institute of Technology (IIT)（杭州电子科技大学; 浙江大学; 纽约大学; 意大利理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出RoboAssist框架，通过非对称双轨表示和跨层安全架构，实现长时程手术辅助中的人形机器人交互式规划，在模拟场景中验证了多阶段任务完成、适应性及重规划效率。
AI 中文摘要
长时程手术辅助要求人形机器人在规划和执行过程中与不断变化的人类活动协调，同时保持安全性。我们提出了RoboAssist，一个用于交互式人形机器人规划的基于智能体的框架，该框架集成了工作流推理、任务协调和跨层安全。其核心是一种非对称双轨表示，将部分观察到的人类过程状态与可执行的机器人任务序列分离。通过在线更新人类过程估计、场景上下文和任务依赖关系，RoboAssist在工作流请求变化时重新验证剩余任务序列，并仅重新规划受影响的后续部分。跨层安全架构结合了预防性导航调节、近距离交接过程中的反应性调节以及独立的全身运行时监督。这种设计在整个执行过程中将在线任务协调与安全约束耦合。我们在Unitree G1人形机器人上，在长时程、多阶段模拟手术辅助场景中展示了该框架，这些场景涵盖多模态交互、器械处理、医疗物资运输、导航和安全的人机交接。实验表明，该框架能够完成多阶段任务并适应工作流请求的变化。一项针对性的完全重规划消融实验表明，残余重规划减少了计划更新延迟和更新后的令牌使用量。单独的安全实验证明了导航、交接和运行时监督方面的互补保护。更多结果和演示可在线获取，网址为https://this URL。
英文摘要
Long-horizon surgical assistance requires humanoid robots to coordinate with evolving human activities while maintaining safety across planning and execution. We present RoboAssist, an agent-based framework for interactive human-humanoid planning that integrates workflow reasoning, task coordination, and cross-layer safety. At its core is an asymmetric dual-track representation that separates partially observed human process states from executable robot task sequences. By updating human-process estimates, scene context, and task dependencies online, RoboAssist revalidates the remaining task sequence and replans only the affected suffix when workflow requests change. A cross-layer safety architecture combines preventive navigation regulation, reactive regulation during close-range handover, and independent whole-body runtime supervision. This design couples online task coordination with safety constraints throughout execution. We demonstrate the framework on a Unitree G1 humanoid robot in long-horizon, multi-stage simulated surgical assistance scenarios encompassing multimodal interaction, instrument handling, medical material transport, navigation, and safe human-robot handover. Experiments show multi-stage task completion and adaptation to workflow-request changes. A targeted full-replanning ablation shows that residual replanning reduces plan-update latency and post-update token usage. Separate safety experiments demonstrate complementary protection across navigation, handover, and runtime supervision. Additional results and demonstrations are available online at https://roboassist.github.io.
CommentsProject Web: https://roboassist.github.io
