MAGIC: Learning Macro-Actions for Online POMDP Planning
This addresses computational bottlenecks in robot decision-making under uncertainty, offering an incremental improvement over existing macro-action methods.
The paper tackles the high computational complexity of POMDP planning for robots under uncertainty by proposing MAGIC, which learns macro-actions offline to improve online planning efficiency, showing significant performance benefits in long-horizon tasks in simulation and on a real robot.
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.