λ₀: ヒューマノイド全身移動操作のためのオープンワールド一人称視点ヒューマンデータのスケーリング
$λ_0$: Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation
500時間の一人称視点ヒューマン動作データセットHumanVerseを構築し、人間からヒューマノイドへの動作変換パイプラインを開発。これを基に全身移動操作の基盤モデルλ₀を学習させ、Unitree G1へのゼロショット転移で実世界タスク62.5%の成功率を達成した。
分類: cs.RO, cs.CV
原文アブストラクト
Humanoid loco-manipulation requires coordinated whole-body interaction with the physical world, yet collecting robot demonstrations at scale remains costly. Human demonstrations offer a scalable alternative, but existing egocentric human supervision often omits locomotion and posture, while embodiment differences and controller dynamics hinder accurate end-effector execution. We introduce HumanVerse, a 500-hour dataset of open-world human loco-manipulation pairing egocentric video with synchronized body and hand motion. Our human-to-humanoid pipeline aligns camera viewpoints and arm appearance, and refines motion references and controller inputs offline to reduce end-effector errors while preserving whole-body coordination. We validate the pipeline through zero-shot transfer to Unitree G1 on representative whole-body manipulation and long-range loco-manipulation tasks, without target-task robot demonstrations. Building on this pipeline, we develop $λ_0$, a humanoid foundation model for whole-body loco-manipulation, with robot-aligned human demonstrations providing supervision for mid-training. Increasing whole-body human data consistently improves downstream task progress. $λ_0$ achieves 90.0% success on SIMPLE and 62.5% across real-world loco-manipulation tasks. We expect this study to guide scalable whole-body human data collection toward humanoid foundation models for richer interaction with the physical world. Homepage: https://lambdahumanoid.github.io/