*Equal contribution
From loco-motion to dextrous manipulation, humanoid robots have made remarkable strides in demonstrating complex full-body capabilities. However, the majority of current robot learning datasets and benchmarks mainly focus on stationary robot arms, and the few existing humanoid datasets are either confined to fixed environments or limited in task diversity, often lacking human-humanoid interaction and lower-body locomotion. Moreover, there are a few standardized evaluation platforms for benchmarking learning-based policies on humanoid data. In this work, we present Humanoid Everyday, a large-scale and diverse humanoid manipulation dataset characterized by extensive task variety involving dextrous object manipulation, human-humanoid interaction, locomotion-integrated actions, and more. Leveraging a highly efficient human-supervised teleoperation pipeline, Humanoid Everyday aggregates high-quality multimodal sensory data, including RGB, depth, LiDAR, and tactile inputs, together with natural language annotations, comprising 10.3k trajectories and over 3 million frames of data across 260 tasks across 7 broad categories. In addition, we conduct an analysis of representative policy learning methods on our dataset, providing insights into their strengths and limitations across different task categories. For standardized evaluation, we introduce a cloud-based evaluation platform that allows researchers to seamlessly deploy their policies in our controlled setting and receive performance feedback. By releasing Humanoid Everyday along with our policy learning analysis and a standardized cloud-based evaluation platform, we intend to advance research in general-purpose humanoid manipulation and lay the groundwork for more capable and embodied robotic agents in real-world scenarios.
Humanoid Everyday Overview. Humanoid Everyday covers 260 tasks across 7 distinct categories of humanoid manipulation tasks with rich multimodal information recorded at 30Hz, and provides a cloud-based evaluation platform for standardized policy deployment.
Task Distribution. Distribution of tasks and skill categories in the Humanoid Everyday Dataset.
Loco-Manipulation
Deformable Manipulation
Articulated Manipulation
Tool Use
High-Precision Manipulation
Human-Robot Interaction
We provide a cloud-based evaluation platform that allows researchers to seamlessly deploy their policies in our controlled setting and receive performance feedback. Click the button below to visit our evaluation website.
Coming Soon@article{zhao2025humanoid-everyday,
author = {Zhenyu Zhao and Hongyi Jing and Xiawei Liu and Jiageng Mao and Abha Jha and Hanwen Yang and Rong Xue and Sergey Zakharov and Vitor Guizilini and Yue Wang},
title = {Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation},
year = {2025},
primaryClass={cs.RO},
}