Unifying Deep Predicate Invention with Pre-trained Foundation Models

Abstract:Long-horizon robotic tasks are hard due to continuous state-action spaces and sparse feedback. Symbolic world models help by decomposing tasks into discrete predicates that capture object properties and relations. Existing methods learn predicates either top-down, by prompting foundation models without data grounding, or bottom-up, from demonstrations without high-level priors. We introduce UniPred, a bilevel learning framework that unifies both. UniPred uses large language models (LLMs) to propose predicate effect distributions that supervise neural predicate learning from low-level data, while learned feedback iteratively refines the LLM hypotheses. Leveraging strong visual foundation model features, UniPred learns robust predicate classifiers in cluttered scenes. We further propose a predicate evaluation method that supports symbolic models beyond STRIPS assumptions. Across five simulated and two real-robot domains, UniPred achieves 2-4 times higher success rates than top-down methods and 3-4 times faster learning than bottom-up approaches, advancing scalable and flexible symbolic world modeling for robotics. For more information and project materials (video, code...) visit this https URL

Robotics (cs.RO)

arXiv:2512.17992 [cs.RO]

(or arXiv:2512.17992v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2512.17992 Focus to learn more arXiv-issued DOI via DataCite

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