Learning to Predict Action Feasibility for Task and Motion Planning in 3D Environments

Abstract

In Task and motion planning (TAMP), symbolic search is combined with continuous geometric planning. A task planner finds an action sequence while a motion planner checks its feasibility and plans the corresponding sequence of motions. However, due to the high combinatorial complexity of discrete search, the number of calls to the geometric planner can be very large. Previous works [1] [2] leverage learning methods to efficiently predict the feasibility of actions, much like humans do, on tabletop scenarios. This way, the time spent on motion planning can be greatly reduced. In this work, we generalize these methods to 3D environments, thus covering the whole workspace of the robot. We propose an efficient method for 3D scene representation, along with a deep neural network capable of predicting the probability of feasibility of an action. We develop a simple TAMP algorithm that integrates the trained classifier, and demonstrate the performance gain of using our approach on multiple problem domains. On complex problems, our method can reduce the time spent on geometric planning by up to 90%.

Learning Proofs of Motion Planning Infeasibility

“…In deterministic sampling-based motion planning, if no plan is found, then either no solution exists or a solution exists only through some narrow passages [28]- [31]. Previous works have also proposed learning-based methods to predict infeasible plans [32]- [36]. All of these methods do not provide exact plan non-existence guarantees.…”