Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human
Overview
This research presents Ropedia W0, the first egocentric loco-manipulation world foundation model spanning humans and robots with diverse embodiments. The model is trained on 10k hours of human egocentric data and 3,000 hours of robot data with unified multimodal annotations. It achieves state-of-the-art performance across five cross-embodiment benchmarks and supports real-time VR interaction after distillation.
From arXiv Robotics — research abstracts
Event evidence and corrections
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Reported quantity · egocentric human data hours: 10000 hours · Basis not reported
“ents. We curate Xp-Data, comprising 10k hours of egocentric human data and 3k hours of robot data with unified multimodal annotations and systematic quality control. A unified kinematic representation encodes locomotion through camera poses and manipulation through projected skeletons, with robot skeletons combining embodiment-specific forward kinematics with extensive manual keypoint annotation and correction. We al”
Exact source · revision 1Source owner not reported
Reported quantity · robot data hours: 3000 hours · Basis not reported
“ents. We curate Xp-Data, comprising 10k hours of egocentric human data and 3k hours of robot data with unified multimodal annotations and systematic quality control. A unified kinematic representation encodes locomotion through camera poses and manipulation through projected skeletons, with robot skeletons combining embodiment-specific forward kinematics with extensive manual keypoint annotation and correction. We al”
Exact source · revision 1Source owner not reported
Reported quantity · cross-embodiment benchmarks: 5 other · Basis not reported
“so introduce Xp-Teleop, a long-horizon loco-manipulation benchmark based on high-quality dexterous humanoid teleoperation data. Evaluations demonstrate state-of-the-art performance across five cross-embodiment benchmarks (DreamDojo, $\Psi_0$, $\omega$-HOME, Xp-Teleop, and AgiBot) and on held-out human egocentric video, from zero-shot inference to few-shot and full-data post-training. Scaling human pre-training data i”
Exact source · revision 1Source owner not reported
Evidence dependency changes (3)
- 2026-10-30T22:17:05.241Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-30T22:16:50.387Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
- 2026-10-21T13:05:47.003Z · evidence updated · source revision 1. Evidence extraction was updated; current source attributions are shown above.
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- arXiv Robotics — research abstractsSignalRopedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience
This research presents Ropedia W0, the first egocentric loco-manipulation world foundation model spanning humans and robots with diverse embodiments. The model is trained on 10k hours of human egocentric data and 3,000 hours of robot data with unified multimodal annotations. It achieves state-of-the-art performance across five cross-embodiment benchmarks and supports real-time VR interaction after distillation.
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