  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.
