使用具有可证明保证的不准确模型进行规划和执行
Planning and Execution using Inaccurate Models with Provable Guarantees
摘要：
现代规划问题中用于模拟现实世界动作执行结果的模型正变得越来越复杂，从基于物理推理的模拟器到预先计算的分析运动基元。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导致执行过程中的高度次优甚至不完整的行为。在本文中，我们提出了CMAX的方法交错规划和执行。CMAX在实际执行过程中在线调整其规划策略，以解决规划过程中动态的任何差异，而无需更新模型的动态。这是通过偏置规划器远离其动态被发现为不准确建模的过渡来实现的，从而导致机器人行为尽管具有不准确的模型，但仍试图完成任务。我们提供了可证明的保证的完整性和效率的建议的规划和执行框架的模型的特定假设下，小和大的状态空间。我们的方法CMAX被证明是有效的经验，在模拟机器人任务，包括4D平面推动，并在真正的机器人实验中使用PR2涉及的3D拾取和放置任务的对象的质量是不正确的建模，和7D手臂规划任务的关节之一是不操作的动态差异。我们的物理机器人实验视频可以在https://youtu.be/eQmAeWIhjO8上找到
英文摘要：
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 https://youtu.be/eQmAeWIhjO8
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
