Humanoid Everyday：面向开放世界人形机器人操作的综合机器人数据集
Humanoid Everyday: A Comprehensive Robotic Dataset for Open-World Humanoid Manipulation
AI总结：
提出Humanoid Everyday数据集，包含10.3k轨迹、260个任务的多模态数据，用于人形机器人灵巧操作、人机交互和移动操作研究，并配套云评估平台。
摘要：
从运动到灵巧操作，人形机器人在展示复杂的全身能力方面取得了显著进展。然而，当前大多数机器人学习数据集和基准主要关注固定机器人臂，少数现有人形数据集要么局限于固定环境，要么任务多样性有限，通常缺乏人机交互和下肢运动。此外，缺乏用于在人形数据上对基于学习的策略进行基准测试的标准化评估平台。在这项工作中，我们提出了Humanoid Everyday，一个大规模且多样化的人形操作数据集，其特点是涉及灵巧物体操作、人机交互、运动集成动作等广泛的任务多样性。利用高效的人工监督遥操作流水线，Humanoid Everyday聚合了高质量的多模态感官数据，包括RGB、深度、LiDAR和触觉输入，以及自然语言注释，包含10.3k条轨迹和超过300万帧数据，涵盖7个大类共260个任务。此外，我们对数据集上的代表性策略学习方法进行了分析，提供了它们在不同任务类别中的优势和局限性的见解。为了标准化评估，我们引入了一个基于云的评估平台，允许研究人员在我们的受控环境中无缝部署他们的策略并接收性能反馈。通过发布Humanoid Everyday以及我们的策略学习分析和标准化的基于云的评估平台，我们旨在推进通用人形操作的研究，并为现实世界中更有能力和具身化的机器人代理奠定基础。我们的数据集、数据收集代码和云评估网站在我们的项目网站上公开发布。
英文摘要：
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. Our dataset, data collection code, and cloud evaluation website are made publicly available on our project website.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
