citekey: "teamLambda0ScalingOpenWorldEgocentricHumanData2026" title: "\(\lambda_0\): Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation" authors: ["humanoid Team"] year: 2026 venue: "arXiv" url: "https://arxiv.org/abs/2610.18950" doi: "10.48550/arXiv.2610.18950" tags: [paper, embodied-ai, "cs.CV", "cs.RO"] status: unread rating: created: 2026-10-16
\(\lambda_0\): Scaling Open-World Egocentric Human Data for Humanoid Loco-Manipulation
Abstract
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 \(\lambda_0\), 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. \(\lambda_0\) 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/
TL;DR
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.