Planning and Execution using Inaccurate Models with Provable Guarantees
Planning and Execution using Inaccurate Models with Provable Guarantees
复制标题
使用具有可证明保证的不准确模型进行规划和执行
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
Maxim Likhachev
中科院分区：
文献类型：
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作者：
Anirudh Vemula;Yash Oza;J. Bagnell;Maxim Likhachev
现代规划问题中用于模拟真实世界动作执行结果的模型正变得越来越复杂，从进行基于物理的推理的模拟器到预计算的解析运动基元。然而，在现实世界中运行的机器人在执行之前经常面临这些模型没有建模的情况。这种不完美的建模可能会导致执行过程中的行为非常不理想，甚至不完整。在本文中，我们提出了一种交织规划和执行的CMAX方法。CMAX在实际执行过程中在线调整其计划策略，以解决计划过程中的任何动态差异，而不需要更新模型的动态。这是通过使规划者远离其动力学被发现建模不准确的过渡来实现的，从而导致尽管具有不准确的模型但仍试图完成任务的机器人行为。在模型的特定假设下，我们对所提出的规划和执行框架的完备性和效率提供了可证明的保证，对于小状态空间和大状态空间都是如此。我们的方法CMAX在包括4D平面推送在内的模拟机器人任务中以及在使用PR2的真实机器人实验中被证明是有效的，其中包括3D拾取放置任务和7D手臂规划任务，其中3D拾取放置任务中对象的质量被错误地建模，而7D手臂规划任务中的一个关节无法操作导致动力学上的差异。我们的物理机器人实验的视频可以在以下的https URL中找到
Models used in modern planning problems to simulate outcomes of real world action executions are becoming increasingly complex, ranging from simulators that do physics-based reasoning to precomputed analytical motion primitives. However, robots operating in the real world often face situations not modeled by these models before execution. This imperfect modeling can lead to highly suboptimal or even incomplete behavior during execution. In this paper, we propose CMAX an approach for interleaving planning and execution. CMAX adapts its planning strategy online during real-world execution to account for any discrepancies in dynamics during planning, without requiring updates to the dynamics of the model. This is achieved by biasing the planner away from transitions whose dynamics are discovered to be inaccurately modeled, thereby leading to robot behavior that tries to complete the task despite having an inaccurate model. We provide provable guarantees on the completeness and efficiency of the proposed planning and execution framework under specific assumptions on the model, for both small and large state spaces. Our approach CMAX is shown to be efficient empirically in simulated robotic tasks including 4D planar pushing, and in real robotic experiments using PR2 involving a 3D pick-and-place task where the mass of the object is incorrectly modeled, and a 7D arm planning task where one of the joints is not operational leading to discrepancy in dynamics. The video of our physical robot experiments can be found at this https URL