歩いた場所を記憶せよ：脚式ロボットのエンドツーエンド探索のための空間記憶学習
Remember Where You Walk: Learning Spatial Memory for End-to-End Legged Robot Exploration
脚式ロボットの迷路探索向けに、位置ベースの記憶セルに情報を保存するLocal Spatial Matrix Memory（LSMM）を提案し、長期記憶を必要とする探索で従来手法より優れた地図再構成性能を示した。
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著者: Leon Magnus, Nico Bohlinger, Jan Peters, Oleg Arenz
分類: cs.RO
原文アブストラクト
Humanoids and other legged robots operating in complex environments must remember information that leaves their field of view and only becomes relevant much later. Reinforcement learning policies address this using memory networks such as LSTMs that are trained end-to-end with the policy to memorize such task-relevant aspects. Yet, such architectures often fail to preserve sufficient information over long horizons. In this work, we propose a network architecture based on Hadamard memory that specifically targets robot navigation. Our Local Spatial Matrix Memory (LSMM) enables RL agents to store generic task-relevant information at specific, position-based memory cells, enabling flexible and spatially grounded memorization. We evaluate LSMM on the task of autonomous maze exploration, where it matches the maze coverage of strong recurrent and finite-history baselines while being the only architecture that remembers the maze layout, reducing the reconstruction error threefold.