Sequence-Based Plan Feasibility Prediction for Efficient Task and Motion Planning

Understand

We present a learning-enabled Task and Motion Planning (TAMP) algorithm for solving mobile manipulation problems in environments with many articulated and movable obstacles.

• Our idea is to bias the search procedure of a traditional TAMP planner with a learned plan feasibility predictor.
• The core of our algorithm is PIGINet, a novel Transformer-based learning method that takes in a task plan, the goal, and the initial state, and predicts the probability of finding motion trajectories associated with the task plan.
• We integrate PIGINet within a TAMP planner that generates a diverse set of high-level task plans, sorts them by their predicted likelihood of feasibility, and refines them in that order.