RoboMoCap

A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning

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Egocentric vision. High-fidelity whole-body motion.

RoboMoCap pairs multimodal first-person observations with optical whole-body motion capture for humanoid robot learning. With six head-mounted and two wrist-mounted cameras, the dataset captures diverse locomotion and loco-manipulation behaviours, from everyday walking to dynamic movements and complex object interactions.

Beyond what egocentric cameras can see. While many egocentric datasets focus primarily on hand-object interactions, RoboMoCap emphasises locomotion and loco-manipulation. By pairing first-person observations with high-fidelity optical motion capture, it provides accurate supervision for lower-body movement, foot placement, and whole-body coordination during navigation and object interactions.

Dataset Overview

• 200+ hours of recording
• 14 participants
• 5TB+ of data
• 290+ captured actions

Data Modalities

Modality	Description
Egocentric sensing (RoboCap)	Six head-mounted cameras provide forward, downward, and side views, alongside IMU, magnetometer measurements, and WAV audio.
Wrist sensing (RoboWrist)	Left and right wrist cameras record close-range hand–object interactions, alongside IMU and magnetometer measurements.
Whole-body motion	A 53-marker NOKOV optical mocap setup records whole-body motion at 120 Hz, including leg and foot movement outside the wearable camera views.
External video	NOKOV's third-person video records the participant, manipulated objects, and the surrounding space.
Object motion	Reflective markers track manipulated objects alongside the participant's body motion.
Object geometry	Textured 3D scans preserve the shape and appearance of objects used in the recordings.
Hand pose	Hand pose complements wrist video to describe hand movements during interaction.

License

This dataset is licensed under CC BY 4.0.

Citation

If you use this dataset, please cite:

@misc{robomocap,
  title = {RoboMoCap: A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning},
  howpublished = {\url{https://bitrobot-foundation.github.io/robomocap/}}
}