Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models
Imitation learning has emerged as a powerful paradigm for learning visuomotor policies, but its generalisation and stability are limited by the scale and quality of demonstration data needed. A promising direction is to leverage more abundant but heterogeneous data sources, which differ in action space and often lack action labels altogether. Existing co-training approaches that combine heterogeneous data sources rely on heuristic and hand-engineered alignment techniques. In contrast, we argue that action representations should be grounded in prediction: actions that produce the same effect on the environment should share the same representation, regardless of their sources. To this end, ...
| Authors | Tianyou Wang, Anson Lei, Joe Watson et al. |
|---|---|
| Primary category | cs.RO |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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