预期性任务与运动规划
Anticipatory Task and Motion Planning
AI总结：
提出预期性任务与运动规划，利用学习模型估计未来成本，指导基于模型的规划器选择计划，避免副作用并最小化总成本，在模拟和真实机器人上显著提升多任务性能。
摘要：
我们考虑一个顺序任务与运动规划（TAMP）设置，其中机器人被逐个分配连续空间中的重排类任务，且环境在每次任务之间持续存在。由于缺乏对未来任务的预先了解，现有的（短视的）规划策略会无意中引入副作用，从而阻碍后续任务的完成：例如，通过阻塞未来的访问或操作。我们提出了预期性任务与运动规划，其中来自学习模型的预期未来成本估计指导基于模型的TAMP规划器生成的计划选择，以避免此类副作用，选择既完成任务又最小化总成本的环境配置。在可移动障碍物间导航和橱柜装载领域的模拟多任务部署中，平均每任务成本分别改善了32.7%和16.7%。当提前有时间准备环境时，我们的学习增强规划方法分别改善了83.1%和22.3%。两者都展示了我们方法的价值。最后，我们还在真实世界的Fetch移动操纵器上演示了预期性TAMP。
英文摘要：
We consider a sequential task and motion planning (tamp) setting in which a robot is assigned continuous-space rearrangement-style tasks one-at-a-time in an environment that persists between each. Lacking advance knowledge of future tasks, existing (myopic) planning strategies unwittingly introduce side effects that impede completion of subsequent tasks: e.g., by blocking future access or manipulation. We present anticipatory task and motion planning, in which estimates of expected future cost from a learned model inform selection of plans generated by a model-based tamp planner so as to avoid such side effects, choosing configurations of the environment that both complete the task and minimize overall cost. Simulated multi-task deployments in navigation-among-movable-obstacles and cabinet-loading domains yield improvements of 32.7% and 16.7% average per-task cost respectively. When given time in advance to prepare the environment, our learning-augmented planning approach yields improvements of 83.1% and 22.3%. Both showcase the value of our approach. Finally, we also demonstrate anticipatory tamp on a real-world Fetch mobile manipulator.
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