Data Standards for Humanoid Robotics: The Missing Infrastructure for Physical AI

• URL: http://arxiv.org/abs/2606.19769v1
• Date: Thu, 18 Jun 2026 04:10:16 GMT
• Title: Data Standards for Humanoid Robotics: The Missing Infrastructure for Physical AI
• Abstract summary: We argue that data standards are becoming foundational infrastructure for Physical AI.<n>As AI moves from screens into bodies, data standards must evolve from organizing digital information to structuring physical interaction.
• Score: 24.21963728682223
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The scalability of humanoid robots will depend not only on models and hardware, but also on whether physical experience can accumulate across robots, tasks, organizations, and time. Drawing on the authors' work in developing ISO/WD 26264-1, Humanoid robot datasets -- Part 1: General requirements, within ISO/TC 299/WG 16, this article argues that data standards are becoming foundational infrastructure for Physical AI. We develop three insights. First, humanoid robot data is embodied interaction data, not a collection of isolated digital samples; a useful dataset must preserve the relationship among robot body, action, task, scene, execution trace, and outcome. Second, its value depends on physical coherence: multimodal streams are reusable only when timing, coordinate frames, calibration, kinematics, units, and synchronization assumptions remain inspectable. Third, the main bottleneck is not only data scarcity, but non-cumulative data caused by high collection costs, data silos, and inconsistent evaluation. We argue that humanoid robot data standards address these bottlenecks by making embodied experience interpretable, shareable, traceable, and reusable. A general standard should provide horizontal infrastructure for lifecycle management, metadata, provenance, quality, versioning, and traceability, while capability-specific parts should define domain grammar for manipulation, locomotion, human-robot interaction, cognition, and future humanoid capabilities. As AI moves from screens into bodies, data standards must evolve from organizing digital information to structuring physical interaction.

Related papers

• The Robot Data Factory [35.46679263015648]
  We introduce the Robot Data Factory (RDF), a mission-driven infrastructure for continuously generating, validating, benchmarking, and reusing experience.<n>RDF organizes heterogeneous robots and environment-specific training grounds through reproducible missions, skill curricula, synchronized multimodal sensing, external ground truth, an agentic robot network, data pipelines, and living benchmarks.<n>We further formalize robot experience and its quality, introduce a mission-task-skill-episode-dataset-benchmark-capability hierarchy, and derive quantitative scaling laws and an algorithmic procedure connecting robot fleet size, sensor rates, storage, learning representations, tokenization, training compute
  arXiv  Detail & Related papers  (2026-09-15T06:30:11Z)
• From Passive Video to Editable Experience: Physically Grounded Experience Synthesis for Embodied Intelligence [2.160835522257474]
  Pegasus is a framework that bridges the embodiment gap between human morphology and robot hardware.<n>A Task Graph extracted from human videos is transformed into a Robot Planning Graph for robot-conditioned video generation.<n>We evaluate Pegasus across a range of egocentric manipulation benchmarks, including GTEA Gaze+ and EPIC-KITCHENS-100.
  arXiv  Detail & Related papers  (2026-07-29T13:35:23Z)
• Data Pyramid for Embodied Manipulation [135.16063649795234]
  Embodied agents require data that couple observations with physical states and actions.<n>We organize the embodied data ecosystem as a "pyramid" spanning five complementary sources.
  arXiv  Detail & Related papers  (2026-07-27T17:59:58Z)
• ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset [51.60529216343344]
  ACME: A Cross-cultural, Multi-Embodiment dataset for social navigation.<n>ACME is a large-scale data collection effort across 8 sites in 5 countries, using 7 robot embodiments.<n>Unlike prior datasets, it focuses on capturing goal-driven social navigation behavior in complex social scenarios with explicit robot-crowd interaction through robot speech.
  arXiv  Detail & Related papers  (2026-07-24T04:23:21Z)
• DataLadder: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid [63.45560198934907]
  Generalist robot policies require trustworthy evaluation and robot-usable training data.<n>We present DataLadder, a simulation-enabled interconversion toolchain for human-robot aligned model evaluation and data generation.<n>We package the core reconstruction, simulation, rendering, and realism-augmentation modules as cloud services on JD Cloud.
  arXiv  Detail & Related papers  (2026-06-15T14:21:35Z)
• EgoVerse: An Egocentric Human Dataset for Robot Learning from Around the World [63.2070298441355]
  EgoVerse is a collaborative platform for human data-driven robot learning.<n>We release 1,362 hours (80k episodes) of human demonstrations spanning 1,965 tasks, 240 scenes, and 2,087 unique demonstrators.<n>We conduct a large-scale study of human-to-robot transfer with experiments replicated across multiple labs, tasks, and robot embodiments.
  arXiv  Detail & Related papers  (2026-04-08T21:27:06Z)
• EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration [67.13034606664333]
  EgoHumanoid is the first framework to co-train a vision-language-action policy using egocentric human demonstrations.<n>A portable system for scalable human data collection is developed.
  arXiv  Detail & Related papers  (2026-02-10T18:59:03Z)
• CEDex: Cross-Embodiment Dexterous Grasp Generation at Scale from Human-like Contact Representations [53.37721117405022]
  Cross-embodiment dexterous grasp synthesis refers to adaptively generating and optimizing grasps for various robotic hands.<n>We propose CEDex, a novel cross-embodiment dexterous grasp synthesis method at scale.<n>We construct the largest cross-embodiment grasp dataset to date, comprising 500K objects across four types with 20M total grasps.
  arXiv  Detail & Related papers  (2025-09-29T12:08:04Z)
• HHI-Assist: A Dataset and Benchmark of Human-Human Interaction in Physical Assistance Scenario [63.77482302352545]
  HHI-Assist is a dataset comprising motion capture clips of human-human interactions in assistive tasks.<n>Our work has the potential to significantly enhance robotic assistance policies.
  arXiv  Detail & Related papers  (2025-09-12T09:38:17Z)
• Curate, Connect, Inquire: A System for Findable Accessible Interoperable and Reusable (FAIR) Human-Robot Centered Datasets [2.9620440248101967]
  The rapid growth of AI in robotics has amplified the need for high-quality, reusable datasets.<n>The landscape of open data in the field is uneven due to a lack of curation standards and consistent publication practices.<n>This paper presents a curation and access system with two main contributions.
  arXiv  Detail & Related papers  (2025-05-30T20:48:32Z)
• Exploring the Potential of Robot-Collected Data for Training Gesture Classification Systems [40.34185280098731]
  This study investigates the feasibility of utilizing robot-collected data to train classification systems. We compare the classification performance of the trained systems using both human-recorded and robot-recorded data.
  arXiv  Detail & Related papers  (2024-05-07T11:58:34Z)
• Where is my hand? Deep hand segmentation for visual self-recognition in humanoid robots [129.46920552019247]
  We propose the use of a Convolution Neural Network (CNN) to segment the robot hand from an image in an egocentric view. We fine-tuned the Mask-RCNN network for the specific task of segmenting the hand of the humanoid robot Vizzy.
  arXiv  Detail & Related papers  (2021-02-09T10:34:32Z)
• Human-robot co-manipulation of extended objects: Data-driven models and control from analysis of human-human dyads [2.7036498789349244]
  We use data from human-human dyad experiments to determine motion intent which we use for a physical human-robot co-manipulation task. We develop a deep neural network based on motion data from human-human trials to predict human intent based on past motion.
  arXiv  Detail & Related papers  (2020-01-03T21:23:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.