Receding Horizon Task and Motion Planning in Dynamic Environments

Complex manipulation tasks in crowded environments require careful integration of symbolic reasoning and motion planning. This problem, commonly defined as Task and Motion Planning (TAMP), is even more challenging if the working space is dynamic and perceived with noisy, non-ideal sensors. In this work, we propose an online, approximated TAMP method that combines a geometric reasoning module and a motion planner with a standard task planner in a receding horizon fashion. Our approach iteratively solves a reduced planning problem over a receding window of a limited number of future actions during the implementation of the actions. At each iteration, only the first action of the horizon is actually implemented, then the window is moved forward and the problem is solved again. This procedure allows to naturally take into account the dynamic changes on the scene while ensuring good runtime performance. We validate our approach within extensive simulated experiments that show that our approach is able to deal with unexpected random changes in the environment configuration while ensuring comparable performance with respect to other recent TAMP approaches in solving traditional, static problems. We release with this paper the open-source implementation of our method.