LEAP: 四足歩行ナビゲーションのための創発的能動知覚の学習
LEAP: Learning Emergent Active Perception for Quadruped Navigation
危険地形での目標探索ナビゲーションにおいて、タスク目的のみから視線制御を創発させる能動知覚ポリシーを学習し、四足歩行の運動制御にも直接適用可能であることを示した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Ü. Bora Gökbakan, Stéphane Caron, Philippe Souères
分類: cs.RO, cs.CV
原文アブストラクト
Active perception allows autonomous agents to select their viewpoints rather than passively process the viewpoints given to them, enabling them to target where to reduce uncertainty about their environment. Learned systems typically encourage this behavior with hand-designed proxy objectives, such as coverage or curiosity bonuses, that may conflict with the task. In this work, we propose a method to learn emergent active perception (LEAP) without augmentation of the task objective. We formulate the problem of goal-oriented navigation over hazardous terrains with goals that must be discovered visually. We then propose an architecture for navigation policies with active perception, and train them on a terrain curriculum where task pressure alone leads to the emergence of gaze control. Key to this emergence, LEAP works on a gaze-invariant representation that integrates depth images into egocentric belief maps. We validate its performance in held-out evaluation scenarios, where it achieves a 92.7% success rate, compared to 74.2% for scripted or 34.5% for passive perception, and comes within 4.6 points of a privileged oracle. We validate that LEAP navigation policies, unchanged, can be directly applied to steering quadrupedal locomotion policies in physics simulation.