Sequential decision-making and motion planning for robotic manipulation induce combinatorial complexity. For long-horizon tasks, especially when the environment comprises many objects that can be interacted with, planning efficiency becomes even more important. To plan such long-horizon tasks, we present the RHH-LGP algorithm for combined task and motion planning (TAMP). First, we propose a TAMP approach (based on Logic-Geometric Programming) that effectively uses geometry-based heuristics for solving long-horizon manipulation tasks. We further improve the efficiency of this planner by a receding horizon formulation, resulting in RHH-LGP. We demonstrate the effectiveness and generality of our approach on several long-horizon tasks that require reasoning about interactions with a large number of objects. Using our framework, we can solve tasks that require multiple robots, including a mobile robot and snake-like walking robots, to form novel heterogeneous kinematic structures autonomously.
翻译:机器人操作的顺序决策和动作规划会引发组合复杂。 对于长方位任务, 特别是环境包含许多可以互动的物体时, 规划效率会变得更加重要。 为了规划这种长方位任务, 我们提出 RHH- LGP 算法, 用于任务和动作的混合规划( TAMP ) 。 首先, 我们提议了一种基于逻辑- 测地程序( 以逻辑- 测深程序为基础) 的TAMP 方法, 有效地使用基于几何的超光谱法来解决长方位操作任务。 我们通过一个后方位配置, 从而导致 RHH- LGP, 来进一步提高这个规划器的效率。 我们展示了我们在若干长方位任务上的方法的有效性和一般性, 需要对与大量物体的互动进行推理。 我们可以利用我们的框架, 解决需要多个机器人( 包括移动机器人和蛇状行行机器人) 的任务, 以便自主地形成新型的混合运动结构 。