Rearrangement puzzles are variations of rearrangement problems in which the elements of a problem are potentially logically linked together. To efficiently solve such puzzles, we develop a motion planning approach based on a new state space that is logically factored, integrating the capabilities of the robot through factors of simultaneously manipulatable joints of an object. Based on this factored state space, we propose less-actions RRT (LA-RRT), a planner which optimizes for a low number of actions to solve a puzzle. At the core of our approach lies a new path defragmentation method, which rearranges and optimizes consecutive edges to minimize action cost. We solve six rearrangement scenarios with a Fetch robot, involving planar table puzzles and an escape room scenario. LA-RRT significantly outperforms the next best asymptotically-optimal planner by 4.01 to 6.58 times improvement in final action cost.

翻译：重排拼图是重排问题的变体，其中问题的各个元素可能在逻辑上相互关联。为了高效求解此类拼图，我们提出了一种基于逻辑因子化新状态空间的运动规划方法，该方法通过物体同时可操控关节的因子整合机器人能力。基于该因子状态空间，我们提出了少动作RRT（LA-RRT）规划器，其目标是通过较少的动作解决拼图。我们的核心方法是一种新的路径去碎片化技术，该技术重新排列并优化连续边以最小化动作成本。我们使用Fetch机器人解决了六个重排场景，包括平面桌面拼图和密室逃脱场景。LA-RRT在最终动作成本上显著优于次优渐近最优规划器，性能提升达4.01至6.58倍。