用物理仿真优化机器人任务与运动规划，让真实机器人能直接执行。
Cross-Entropy Optimization of Physically Grounded Task and Motion Plans
- 通过GPU并行物理模拟器生成带动力学和接触约束的运动计划。
- 利用交叉熵优化搜索控制器参数，获得低成本可行解。
- 适合需要精确物理交互的机器人操作任务，如环境借力移动物体。
自主执行任务通常需要机器人规划高层离散动作与底层连续运动。以往的运动与任务规划（TAMP）算法主要关注计算效率、完备性或最优性，但通过简化和抽象使问题可解，代价是所得计划可能忽略物体操作所需的动态特性或复杂接触关系。此外，忽略底层控制器影响的方法难以获得真实系统中可行或最优的计划实现。本文采用GPU并行化物理模拟器，计算包含运动控制器的计划实现，显式考虑动力学及与环境的接触。通过交叉熵优化，采样控制器参数或动作，以获取低代价解。由于使用与真实系统相同的控制器，机器人可直接执行所计算的计划。我们在一组任务中验证了该方法，机器人成功利用环境几何结构移动物体。
原文摘要 · Abstract (English)
Autonomously performing tasks often requires robots to plan high-level discrete actions and continuous low-level motions to realize them. Previous TAMP algorithms have focused mainly on computational performance, completeness, or optimality by making the problem tractable through simplifications and abstractions. However, this comes at the cost of the resulting plans potentially failing to account for the dynamics or complex contacts necessary to reliably perform the task when object manipulation is required. Additionally, approaches that ignore effects of the low-level controllers may not obtain optimal or feasible plan realizations for the real system. We investigate the use of a GPU-parallelized physics simulator to compute realizations of plans with motion controllers, explicitly accounting for dynamics, and considering contacts with the environment. Using cross-entropy optimization, we sample the parameters of the controllers, or actions, to obtain low-cost solutions. Since our approach uses the same controllers as the real system, the robot can directly execute the computed plans. We demonstrate our approach for a set of tasks where the robot is able to exploit the environment's geometry to move an object. Website and code: https://andreumatoses.github.io/research/parallel-realization
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