EgoHTR: 自己中心視点による人間の地形横断の4Dデモンストレーション
EgoHTR: Egocentric 4D Demonstrations of Human Terrain Traversal
自己中心型ウェアラブルと3Dスキャナを用いて、複雑な環境での人間の動作とシーンを整合させた4Dデータセットを構築し、これを活用して人型ロボットの不整地歩行ポリシーを訓練・実機検証した論文。
著者: Alex Brandes, Haig Conti Georges Sajelian, Manthan Patel, Dominik Hollidt, Chenhao Li, Matthias Heyrman, Oliver Hausdoerfer, Manuel Kaufmann, Xi Wang, Jonas Frey, Angela P. Schoellig, Christian Holz, Marc Pollefeys, Marco Hutter
分類: cs.RO, cs.CV
原文アブストラクト
Deploying humanoid robots in unstructured terrain remains an open problem. While classic reinforcement learning struggles with the sheer complexity of real-world interactions, more promising methods leveraging human priors remain limited to models lacking contextual awareness. The restricted motion synthesis is a direct consequence of existing dataset pipelines failing to capture human-scene sequences in challenging environments. To bridge this gap between humanoid learning and scene reconstruction, we introduce the Egocentric Human-Terrain Reconstruction (EgoHTR) dataset. We develop and open-source a reconstruction pipeline capturing 55 scene-aligned 4D human motion sequences in diverse, complex environments using a multi-sensor setup of egocentric wearables and a portable 3D scanner. The resulting dataset comprises over 150k frames, which we evaluate against motion-capture ground truth, demonstrating state-of-the-art accuracy and establishing a rigorous benchmark for human motion analysis and synthesis. Further, we leverage this data to train perceptive locomotion policies, demonstrating hardware deployment on a Unitree G1 for reconstructed reference motions. Our pipeline enables community-driven dataset extensions and factors the problem to help researchers build foundational, context-aware robots that reliably traverse uneven terrain.