Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition
The paper introduces Predicate Action Skills (PACTS), a new approach to learning complex robot behaviors. PACTS jointly models action trajectories and symbolic outcomes, enabling better generalization and skill composition without retraining. This method allows for zero-shot composition of learned skills through effective planning and execution monitoring.
- ▪Learning from Demonstration (LfD) often struggles with generalizing to new skill compositions without retraining.
- ▪PACTS models skills as a joint generative process over action and predicate belief trajectories.
- ▪The approach enhances both action generation and predicate classification, facilitating zero-shot skill composition.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.20648 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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Computer Science > Robotics arXiv:2605.20648 (cs) [Submitted on 20 May 2026] Title:Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition Authors:Benedict Quartey, Sebastian Castro, Eric Rosen, Wil Thomason, George Konidaris, Stefanie Tellex View a PDF of the paper titled Jointly Learning Predicates and Actions Enables Zero-Shot Skill Composition, by Benedict Quartey and 5 other authors View PDF HTML (experimental) 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.
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