Latent Actions from Factorized Transition Effects under Agent Ambiguity
Latent Action Models (LAMs) learn action-like proxies from observation. However, in multi-object or distractor-rich scenes, observations contain not only agent motion but also distractors, camera dynamics, and background changes, making recovery of the underlying action intrinsically ambiguous without supervision. We argue that the appropriate unsupervised target is therefore not the true action itself, but a state-conditioned compositional summary of the transition effects present in the scene, enabling better action alignment and more effective utilization of action supervision. To this end, we propose a two-stage framework. We first pretrain Observed Transition Factorization (OTF) to d...
| Authors | Heejeong Nam, Chandradithya S Jonnalagadda, Harshit Aggarwal et al. |
|---|---|
| Primary category | cs.AI |
| Published | 29 June 2026 |
| Last updated at source | 31 July 2026 |
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