Learning Symbolic Operators for Task and Motion Planning

TL;DR

This paper introduces a relational learning method for symbolic operator acquisition in task and motion planning, improving planning efficiency in robotic domains by learning domain-specific operators.

Contribution

It formalizes the operator learning problem for TAMP and proposes a bottom-up relational learning approach that outperforms existing graph neural network-based methods.

Findings

Learned operators enable efficient planning in complex robotic tasks.

The approach outperforms several baselines, including graph neural network models.

Effective in long-horizon robotic planning scenarios.

Abstract

Robotic planning problems in hybrid state and action spaces can be solved by integrated task and motion planners (TAMP) that handle the complex interaction between motion-level decisions and task-level plan feasibility. TAMP approaches rely on domain-specific symbolic operators to guide the task-level search, making planning efficient. In this work, we formalize and study the problem of operator learning for TAMP. Central to this study is the view that operators define a lossy abstraction of the transition model of a domain. We then propose a bottom-up relational learning method for operator learning and show how the learned operators can be used for planning in a TAMP system. Experimentally, we provide results in three domains, including long-horizon robotic planning tasks. We find our approach to substantially outperform several baselines, including three graph neural network-based model-free approaches from the recent literature. Video: https://youtu.be/iVfpX9BpBRo Code: https://git.io/JCT0g