Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
Learning from Demonstration (LfD) enables robots to learn complex behaviors from expert examples, yet existing approaches often fail to generalize to new compositions of known skills without retraining. Modern generative policies model distributions over action trajectories alone, thus are unable to reason about the symbolic outcomes required for robust composition. We propose that skills should jointly model action trajectories and the symbolic outcomes they induce. To address this gap, we introduce Predicate Action Skills (PACTS), a class of closed-loop visuomotor policies that model skills as a joint generative process over action and predicate belief trajectories, producing coherent a...
| Authors | Benedict Quartey, Sebastian Castro, Eric Rosen et al. |
|---|---|
| Primary category | cs.RO |
| Published | 20 May 2026 |
| Last updated at source | 20 May 2026 |
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