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

arXiv: https://arxiv.org/abs/2606.19769 · HTML: https://mlsanity.com/paper/2606.19769/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 22 references that are in the map.
- First posted on arXiv 18 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset](https://mlsanity.com/paper/2403.12945/) (2024)
- [Learning from Massive Human Videos for Universal Humanoid Pose Control](https://mlsanity.com/paper/2412.14172/) (2024)
- [Autonomy 2.0: The Quest for Economies of Scale](https://mlsanity.com/paper/2307.03973/) (2023)
- [Towards Large-Scale In-Context Reinforcement Learning by Meta-Training in Randomized Worlds](https://mlsanity.com/paper/2502.02869/) (2025)
- [Rise of the Autonomous Machines](https://mlsanity.com/paper/2106.13987/) (2021)

## Built on by (most cited first, 1 of 1)

- [Behaviora - A Conceptual Architecture for External and Internal Behavior of Robots and Agents](https://mlsanity.com/paper/2609.27536/) (2026)

## Abstract (arXiv)

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.