Task and motion planning is a well-established approach for solving long-horizon robot planning problems. However, traditional methods assume that each task-level robot action, or skill, can be reduced to kinematic motion planning. We address the challenge of combining motion planning with closed-loop motor controllers that go beyond mere kinematic considerations. We propose a novel framework that integrates these policies into motion planning using Composable Interaction Primitives (CIPs), enabling the use of diverse, non-composable pre-learned skills in hierarchical robot planning. We validate our Task and Skill Planning (TASP) approach through real-world experiments on a bimanual manipulator and a mobile manipulator, demonstrating that CIPs allow diverse robots to combine motion planning with general-purpose skills to solve complex, long-horizon tasks.

翻译：任务与运动规划是解决长时域机器人规划问题的成熟方法。然而，传统方法假设每个任务层级的机器人动作（即技能）均可简化为运动学层面的运动规划。本研究针对如何将运动规划与超越单纯运动学考量的闭环运动控制器相结合这一挑战，提出了一种新颖的框架。该框架通过可组合交互基元将此类控制策略整合至运动规划中，从而在分层机器人规划中实现多样化、非组合式预学习技能的调用。我们在双臂操作器与移动操作器上进行了实物实验，验证了所提出的任务与技能规划方法。实验结果表明，可组合交互基元能使不同类型的机器人将运动规划与通用技能相结合，以解决复杂的长时域任务。