物理シミュレーションに基づくタスク・動作計画のクロスエントロピー最適化
Cross-Entropy Optimization of Physically Grounded Task and Motion Plans
GPU並列物理シミュレータとクロスエントロピー法を用いて、ロボットの動作コントローラのパラメータを最適化し、環境との接触や動力学を考慮した実行可能なタスク・動作計画を生成する手法を提案した。
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著者: Andreu Matoses Gimenez, Nils Wilde, Chris Pek, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
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
