RoboMoCap: A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning
Overview
The paper introduces RoboMoCap, a large-scale multimodal dataset combining egocentric vision with marker-based optical motion capture designed for locomotion-centric humanoid learning. It includes over 200 hours of recordings from 14 participants using six head-mounted cameras, wrist-mounted sensors, and full-body optical motion capture at 120 Hz. The dataset covers diverse locomotion primitives, dynamic whole-body movements, and loco-manipulation tasks, providing temporally aligned multimodal observations and metric whole-body motion representations. The authors evaluate existing methods on three benchmark tasks: egocentric full-body motion reconstruction, foot contact and placement estimation, and human-to-humanoid motion retargeting.
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Data origin: human egocentric
“sting egocentric datasets often lack high-fidelity optical motion capture or systematic coverage of diverse loco-manipulation behaviours. To address this gap, we introduce RoboMoCap, a large-scale multimodal dataset combining egocentric vision with marker-based optical motion capture, specifically designed for locomotion-centric humanoid learning. RoboMoCap comprises over 200 hours of recordings from 14 participants,”
Exact source · revision 1Source owner not reported
Reported quantity · recording_hours: 200 hours · Basis not reported
“sting egocentric datasets often lack high-fidelity optical motion capture or systematic coverage of diverse loco-manipulation behaviours. To address this gap, we introduce RoboMoCap, a large-scale multimodal dataset combining egocentric vision with marker-based optical motion capture, specifically designed for locomotion-centric humanoid learning. RoboMoCap comprises over 200 hours of recordings from 14 participants,”
Exact source · revision 1Source owner not reported
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- arXiv Robotics — research abstractsSignalRoboMoCap: A Large-Scale Whole-Body Motion Capture Dataset with Egocentric Vision for Robot Learning
The paper introduces RoboMoCap, a large-scale multimodal dataset combining egocentric vision with marker-based optical motion capture designed for locomotion-centric humanoid learning. It includes over 200 hours of recordings from 14 participants using six head-mounted cameras, wrist-mounted sensors, and full-body optical motion capture at 120 Hz. The dataset covers diverse locomotion primitives, dynamic whole-body movements, and loco-manipulation tasks, providing temporally aligned multimodal observations and metric whole-body motion representations. The authors evaluate existing methods on three benchmark tasks: egocentric full-body motion reconstruction, foot contact and placement estimation, and human-to-humanoid motion retargeting.
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