Robot Self-Improvement via Human-Video Dynamics Models
A central question in robot learning is how to acquire skills from the kinds of data that humans learn from: passive observation, embodied practice, and the experience of failure. Human videos provide the first of these in abundance, and prior work has shown they can initialize useful policies. Far less clear is whether they can support the second and third: whether priors extracted from human videos can ground a robot's own attempts well enough to evaluate them, correct them, and improve from them. In this work, we show that human videos can be used to learn embodiment-agnostic action, dynamics, and value representations that transfer across robot embodiments, providing the predictive fo...
| Authors | Hanzhi Chen, Anran Zhang, Simon Schaefer et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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