連続行動空間におけるマルチオブジェクト探索のための長期的ロボット探索戦略の学習
Learning Long-Horizon Robot Exploration Strategies for Multi-Object Search in Continuous Action Spaces
強化学習により、短期・長期の推論を単一モデルに統合し、連続行動空間で複数物体を探索する手法を提案。限られたデータで未知の住環境に汎化し、実世界のオフィス環境へのゼロショット転移も実証した。
著者: Fabian Schmalstieg, Daniel Honerkamp, Tim Welschehold, Abhinav Valada
分類: cs.RO
原文アブストラクト
Recent advances in vision-based navigation and exploration have shown impressive capabilities in photorealistic indoor environments. However, these methods still struggle with long-horizon tasks and require large amounts of data to generalize to unseen environments. In this work, we present a novel reinforcement learning approach for multi-object search that combines short-term and long-term reasoning in a single model while avoiding the complexities arising from hierarchical structures. In contrast to existing multi-object search methods that act in granular discrete action spaces, our approach achieves exceptional performance in continuous action spaces. We perform extensive experiments and show that it generalizes to unseen apartment environments with limited data. Furthermore, we demonstrate zero-shot transfer of the learned policies to an office environment in real world experiments.
