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: this https URL
CoHuB: A Simulation Benchmark for Multi-Humanoid Collaboration
Yonsei University（延世大学）；Gwangju Institute of Science and Technology（光州科学技术院）；University of California, Berkeley（加州大学伯克利分校）；Seoul National University（首尔大学）
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing University of Technology（北京工业大学）；Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；JD Explore Academy（京东探索研究院）
Contrastive Representation Learning for Robust Sim-to-Real Transfer of Adaptive Humanoid Locomotion
Adaptive Robotic Controls Lab (ArcLab), Department of Mechanical Engineering, The University of Hong Kong, Hong Kong SAR, China（适应性机器人控制实验室（ArcLab）、机械工程系、香港大学、香港特别行政区，中国）；PNDbotics, China（PNDbotics，中国）
Booster Lab: A Data-Centric Pipeline for Learning Deployable Humanoid Locomotion Policies
Department of Automation, Tsinghua University（清华大学自动化系）；School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University（北京交通大学机械与电子控制工程学院）；Booster Robotics Technology Co., Ltd（北京加速进化科技有限公司）
Gallant: Voxel Grid-based Humanoid Locomotion and Local-navigation across 3D Constrained Terrains
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；University of Science and Technology of China（中国科学技术大学）；University of Tokyo（东京大学）；Shanghai Jiaotong University（上海交通大学）
EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control
Tianjin University（天津大学）；Zhongguancun Academy（中关村学院）；Beihang University（北京航空航天大学）；Zhongguancun Institute of Artificial Intelligence（中关村人工智能研究院）；DeepCybo
ZeroWBC: Learning Natural Whole-Body Humanoid Interaction from Human Egocentric Data
University of Science and Technology of China（中国科学技术大学）；Shanghai AI Laboratory（上海人工智能实验室）；Northwestern Polytechnical University（西北工业大学）；Tsinghua University（清华大学）；Shanghai Jiao Tong University（上海交通大学）；TeleAI, China Telecom（TeleAI，中国电信）
HWC-Loco: A Hierarchical Whole-Body Control Approach to Robust Humanoid Locomotion
The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Harbin Institute of Technology（哈尔滨工业大学）；DexForce Technology（DexForce科技）；Southeast University（东南大学）
We present an integrated approach to locomotion and balancing of humanoid robots based on direct centroidal control. Our method uses a five-mass description of a humanoid. It generates whole-body motions from desired foot trajectories and centroidal parameters of the robot. A set of simplified models is used to formulate general and intuitive control laws, which are then applied in real-time for estimating and regulating the center of mass position and orientation of the multibody's principal axes of inertia. The combination of proposed algorithms produces a stretched-leg gait with naturally l…
Humanoid Loco-manipulation Planning based on Graph Search and Reachability Maps
CNRS-AIST JRL (Joint Robotics Laboratory)（CNRS-AIST联合机器人实验室）；IRL（机器人研究所）；National Institute of Advanced Industrial Science and Technology (AIST)（国家先进工业科学与技术研究院）
This paper presents a new learning framework that leverages the knowledge from imitation learning, deep reinforcement learning, and control theories to achieve human-style locomotion that is natural, dynamic, and robust for humanoids. We proposed novel approaches to introduce human bias, i.e. motion capture data and a special Multi-Expert network structure. We used the Multi-Expert network structure to smoothly blend behavioral features, and used the augmented reward design for the task and imitation rewards. Our reward design is composable, tunable, and explainable by using fundamental concep…
Towards a General Humanoid Loco-Manipulation Model via Egocentric Whole-Body Human Data Pretraining
Alibaba Group（阿里巴巴集团）；Sichuan University（四川大学）；Shanghai Innovation Institute（上海创新研究院）；Beihang University（北京航空航天大学）；Hong Kong University of Science and Technology（香港科技大学）
Istituto Italiano di Tecnologia（意大利理工学院）；IRI CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学机器人研究所）；AIST（日本产业技术综合研究所）；LAAS-CNRS（法国国家科学研究中心系统分析与架构实验室）；DIBRIS-UNIGE（热那亚大学计算机、生物工程、机器人与系统工程系）
The future where the industrial shop-floors witness humans and robots working in unison and the domestic households becoming a shared space for both these agents is not very far. The scientific community has been accelerating towards that future by extending their research efforts in human-robot interaction towards human-robot collaboration. It is possible that the anthropomorphic nature of the humanoid robots could deem the most suitable for such collaborations in semi-structured, human-centered environments. Wearable sensing technologies for human agents and efficient human-aware control str…
GM-Loco: Terrain-Adaptive Humanoid Locomotion on Granular Media
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；School of Physics, Georgia Institute of Technology（佐治亚理工学院物理学院）；Northeastern University（东北大学）
Biomechanical Comparisons Reveal Divergence of Human and Humanoid Gaits
Zhejiang University（浙江大学）；School of Engineering, Westlake University（西湖大学工程学院）；Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences（中国科学院宁波材料技术与工程研究所）
