Long-horizon task and motion planning (TAMP) is notoriously difficult to solve, let alone optimally, due to the tight coupling between the interleaved (discrete) task and (continuous) motion planning phases, where each phase on its own is frequently an NP-hard or even PSPACE-hard computational challenge. In this study, we tackle the even more challenging goal of jointly optimizing task and motion plans for a real dual-arm system in which the two arms operate in close vicinity to solve highly constrained tabletop multi-object rearrangement problems. Toward that, we construct a tightly integrated planning and control optimization pipeline, Makespan-Optimized Dual-Arm Planner (MODAP) that combines novel sampling techniques for task planning with state-of-the-art trajectory optimization techniques. Compared to previous state-of-the-art, MODAP produces task and motion plans that better coordinate a dual-arm system, delivering significantly improved execution time improvements while simultaneously ensuring that the resulting time-parameterized trajectory conforms to specified acceleration and jerk limits.

翻译：长时域任务与运动规划（TAMP）因交错的（离散）任务规划与（连续）运动规划阶段之间的紧密耦合而极其难以求解，更遑论实现最优——每个阶段本身通常就是NP难甚至PSPACE难的计算挑战。本研究针对一个更具挑战性的目标，即对真实双臂系统（其两条机械臂在紧密相邻的空间中协同作业）的任务与运动计划进行联合优化，以解决高度受限的桌面多物体重排问题。为此，我们构建了高度集成的规划与控制优化流水线——双臂最优完工时间规划器（MODAP），该规划器将新型任务规划采样技术与最先进的轨迹优化技术相结合。与以往最先进方法相比，MODAP能生成更优协调双臂系统的任务与运动计划，在显著提升执行时间效率的同时，确保生成的时参轨迹符合指定的加速度与加加速度约束。