Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition

arXiv:2605.20648v1 Announce Type: cross Abstract: 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

Source: arXiv cs.AI — read the full report at the original publisher.

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