MAGIC: Learning Macro-Actions for Online POMDP Planning

TL;DR

MAGIC is a novel method that learns macro-actions offline to improve online POMDP planning efficiency, enabling robots to make better decisions under uncertainty in complex tasks.

Contribution

This paper introduces MAGIC, an end-to-end learned macro-action generator that adapts macro-actions dynamically for improved online POMDP planning performance.

Findings

Learned macro-actions significantly improve planning efficiency.

MAGIC outperforms primitive and handcrafted macro-actions.

Effective in both simulation and real robot tasks.

Abstract

The partially observable Markov decision process (POMDP) is a principled general framework for robot decision making under uncertainty, but POMDP planning suffers from high computational complexity, when long-term planning is required. While temporally-extended macro-actions help to cut down the effective planning horizon and significantly improve computational efficiency, how do we acquire good macro-actions? This paper proposes Macro-Action Generator-Critic (MAGIC), which performs offline learning of macro-actions optimized for online POMDP planning. Specifically, MAGIC learns a macro-action generator end-to-end, using an online planner's performance as the feedback. During online planning, the generator generates on the fly situation-aware macro-actions conditioned on the robot's belief and the environment context. We evaluated MAGIC on several long-horizon planning tasks both in simulation and on a real robot. The experimental results show that the learned macro-actions offer significant benefits in online planning performance, compared with primitive actions and handcrafted macro-actions.