TERRA: Learning Transportable Latent Actions through Temporal Effect Representation and Relational Alignment
Latent actions supervise robot policies with action-like codes inferred from visual transitions, and their usefulness hinges on two questions: what a code keeps from a transition, and whether it still means the same thing when reused in a different initial state. The first is a tension in time: an endpoint difference discards how motion unfolds, while the full sequence admits nuisance variation. The second is left open by reconstruction, which only ever observes a latent together with the state it came from. We argue that both questions can be answered in the same place. TERRA (Temporal Effect Representation and Relational Alignment) describes a transition by a compact temporal effect, it...
| Authors | Tianxingjian Ding, Mubarak Shah, Yu Tian |
|---|---|
| Primary category | cs.CV |
| Published | 7 October 2026 |
| Last updated at source | 7 October 2026 |
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