让机器人同时规划与执行，提升真实环境下的任务鲁棒性
Acting and Planning with Hierarchical Operational Models on a Mobile Robot: A Study with RAE+UPOM
- 共享分层操作模型，规划与执行动态交替进行
- 在真实机器人上实现物体收集任务，抗动作失败与传感器噪声
- 适合需要实时决策的移动操作机器人研究者
机器人任务执行常因符号规划模型与实际运行的丰富控制结构之间不一致而受阻。本文首次在物理机器人上部署了集成的行动-规划系统，该系统在规划与执行中共享分层操作模型，并将反应式执行引擎（RAE）与一种类似UCT的即时蒙特卡洛规划器（UPOM）交错运行。我们在移动机械臂上实现了RAE+UPOM，完成了真实世界中的物体收集任务。实验表明，系统在动作失败和传感器噪声下仍能稳健执行任务，并提供了对交错式决策过程的实证洞察。
原文摘要 · Abstract (English)
Robotic task execution faces challenges due to the inconsistency between symbolic planner models and the rich control structures actually running on the robot. In this paper, we present the first physical deployment of an integrated actor-planner system that shares hierarchical operational models for both acting and planning, interleaving the Reactive Acting Engine (RAE) with an anytime UCT-like Monte Carlo planner (UPOM). We implement RAE+UPOM on a mobile manipulator in a real-world deployment for an object collection task. Our experiments demonstrate robust task execution under action failures and sensor noise, and provide empirical insights into the interleaved acting-and-planning decision making process.
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。