Task and Motion Planning (TAMP) has made strides in complex manipulation tasks, yet the execution robustness of the planned solutions remains overlooked. In this work, we propose a method for reactive TAMP to cope with runtime uncertainties and disturbances. We combine an Active Inference planner (AIP) for adaptive high-level action selection and a novel Multi-Modal Model Predictive Path Integral controller (M3P2I) for low-level control. This results in a scheme that simultaneously adapts both high-level actions and low-level motions. The AIP generates alternative symbolic plans, each linked to a cost function for M3P2I. The latter employs a physics simulator for diverse trajectory rollouts, deriving optimal control by weighing the different samples according to their cost. This idea enables blending different robot skills for fluid and reactive plan execution, accommodating plan adjustments at both the high and low levels to cope, for instance, with dynamic obstacles or disturbances that invalidate the current plan. We have tested our approach in simulations and real-world scenarios.

翻译：任务与运动规划在复杂操作任务中取得了进展，但规划解的执行鲁棒性仍被忽视。本文提出了一种反应式任务与运动规划方法，以应对运行时的不确定性和干扰。我们将主动推断规划器（用于自适应高层动作选择）与新型多模态模型预测路径积分控制器（用于底层控制）相结合，形成一种同时调整高层动作与底层运动的方案。主动推断规划器生成多个备选符号化规划，每个规划对应一个用于M3P2I的成本函数。M3P2I利用物理仿真器进行多样化轨迹展开，通过根据不同样本的成本对它们进行加权来推导最优控制。这一思想实现了不同机器人技能的融合，支持流畅且反应式的规划执行，并在高层和低层同时进行规划调整，以应对例如动态障碍物或使当前规划失效的干扰。我们已在仿真和真实场景中测试了所提方法。