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 t

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ethics.ai (19 June 2026), “Imitation from Heterogeneous Demonstrations using Grounded Latent-Action World Models,” evidence record 756, https://ethics.ai/record/756 (originally published by arXiv).

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