Learning Quickly to Plan Quickly Using Modular Meta-Learning

TL;DR

This paper introduces a modular meta-learning approach to quickly adapt sampling functions called specializers for multi-object manipulation planning, enabling rapid and efficient planning across diverse tasks.

Contribution

It proposes a novel modular meta-learning method to learn adaptable specializers that improve planning efficiency in multi-object manipulation tasks.

Findings

Specializers can be quickly adapted to new tasks with limited data.

The approach improves planning efficiency in simulated 3D pick-and-place tasks.

Meta-learning enables generalization across multiple tasks.

Abstract

Multi-object manipulation problems in continuous state and action spaces can be solved by planners that search over sampled values for the continuous parameters of operators. The efficiency of these planners depends critically on the effectiveness of the samplers used, but effective sampling in turn depends on details of the robot, environment, and task. Our strategy is to learn functions called "specializers" that generate values for continuous operator parameters, given a state description and values for the discrete parameters. Rather than trying to learn a single specializer for each operator from large amounts of data on a single task, we take a modular meta-learning approach. We train on multiple tasks and learn a variety of specializers that, on a new task, can be quickly adapted using relatively little data -- thus, our system "learns quickly to plan quickly" using these specializers. We validate our approach experimentally in simulated 3D pick-and-place tasks with continuous state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a supplementary video.