Discovering State and Action Abstractions for Generalized Task and Motion Planning

TL;DR

This paper introduces an algorithm for learning features and abstractions in continuous robotic task and motion planning, enabling generalized plans that incorporate geometric constraints and improve solver efficiency.

Contribution

It presents a novel method for learning abstractions in continuous TAMP that considers physical constraints, enhancing planning efficiency across multiple problem instances.

Findings

Learned generalized plans from few examples.

Improved search efficiency in TAMP solvers.

Incorporated geometric and physical constraints into plans.

Abstract

Generalized planning accelerates classical planning by finding an algorithm-like policy that solves multiple instances of a task. A generalized plan can be learned from a few training examples and applied to an entire domain of problems. Generalized planning approaches perform well in discrete AI planning problems that involve large numbers of objects and extended action sequences to achieve the goal. In this paper, we propose an algorithm for learning features, abstractions, and generalized plans for continuous robotic task and motion planning (TAMP) and examine the unique difficulties that arise when forced to consider geometric and physical constraints as a part of the generalized plan. Additionally, we show that these simple generalized plans learned from only a handful of examples can be used to improve the search efficiency of TAMP solvers.