论文标题
蒙特卡洛树搜索有效的物体操纵计划
Efficient Object Manipulation Planning with Monte Carlo Tree Search
论文作者
论文摘要
本文提出了一种使用蒙特卡洛树搜索（MCT）来查找接触序列和有效的基于ADMM的轨迹优化算法的有效方法来进行对象操纵计划，以评估候选接触序列的动态可行性。为了加速MCT，我们提出了一种学习目标条件的政策价值网络的方法，以将搜索引导到有前途的节点。此外，操纵特定的启发式方法可以大大减少搜索空间。物理模拟器和实际硬件中的系统对象操作实验证明了我们方法的效率。特别是，由于学到的政策价值网络，我们的方法对长期操纵序列的范围非常有利，从而大大提高了计划的成功率。
This paper presents an efficient approach to object manipulation planning using Monte Carlo Tree Search (MCTS) to find contact sequences and an efficient ADMM-based trajectory optimization algorithm to evaluate the dynamic feasibility of candidate contact sequences. To accelerate MCTS, we propose a methodology to learn a goal-conditioned policy-value network to direct the search towards promising nodes. Further, manipulation-specific heuristics enable to drastically reduce the search space. Systematic object manipulation experiments in a physics simulator and on real hardware demonstrate the efficiency of our approach. In particular, our approach scales favorably for long manipulation sequences thanks to the learned policy-value network, significantly improving planning success rate.
