見えた！：生ピクセルからのエンドツーエンドヒューマノイド歩行学習
Now You See That: Learning End-to-End Humanoid Locomotion from Raw Pixels
生の深度画像からヒューマノイドの歩行をエンドツーエンドで学習するフレームワークを提案し、高忠実度深度センサシミュレーションと視覚認識行動蒸留、地形別報酬設計により、実機での多様な地形適応を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Wandong Sun, Yongbo Su, Leoric Huang, Alex Zhang, Dwyane Wei, Mu San, Daniel Tian, Ellie Cao, Baoshi Cao, Yang Liu, Finn Yan, Ethan Xie, Zongwu Xie
分類: cs.RO
原文アブストラクト
Achieving robust vision-based humanoid locomotion remains challenging due to two fundamental issues: the sim-to-real gap introduces significant perception noise that degrades performance on fine-grained tasks, and training a unified policy across diverse terrains is hindered by conflicting learning objectives. To address these challenges, we present an end-to-end framework for vision-driven humanoid locomotion. For robust sim-to-real transfer, we develop a high-fidelity depth sensor simulation that captures stereo matching artifacts and calibration uncertainties inherent in real-world sensing. We further propose a vision-aware behavior distillation approach that combines latent space alignment with noise-invariant auxiliary tasks, enabling effective knowledge transfer from privileged height maps to noisy depth observations. For versatile terrain adaptation, we introduce terrain-specific reward shaping integrated with multi-critic and multi-discriminator learning, where dedicated networks capture the distinct dynamics and motion priors of each terrain type. We validate our approach on two humanoid platforms equipped with different stereo depth cameras. The resulting policy demonstrates robust performance across diverse environments, seamlessly handling extreme challenges such as high platforms and wide gaps, as well as fine-grained tasks including bidirectional long-term staircase traversal.
