aiqbench analysis
This research introduces a bilevel neuro-symbolic framework that combines probabilistic symbolic planning with continuous effect models to address limitations in robot manipulation tasks. By integrating high-level symbolic rule generation with low-level verification and forward search, the method achieves planning performance comparable to pure continuous approaches while significantly improving efficiency. The work advances machine learning by effectively bridging discrete reasoning with continuous dynamics for complex agent behaviors.
Bilevel Planning with Learned Symbolic Abstractions from Interaction Data
arXiv:2603.08599v2 Announce Type: replace Abstract: Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previous studies have shown that symbolic abstractions can emerge from neural effect predictors trained with a robot's unsupervised exploration. However, these methods rely on deterministic symbolic domains, lack mechanisms to verify the generated symbolic plans, and operate only at the abstract level, often failing to capture the continuous dynamics of the environment. To overcome these limitations, we propose a bilevel neuro-symbolic framework in which learned probabilistic symbolic rules generate candidate plans rapidly at the high level, and learned continuous effect models verify these plans and perform forward search when necessary at the low level. Our experiments on multi-object manipulation tasks demonstrate that the proposed bilevel method outperforms symbolic-only approaches, reliably identifying failing plans through verification, and achieves planning performance statistically comparable to continuous forward search while resolving most problems via efficient symbolic reasoning.
Abstract:Intelligent agents must reason over both continuous dynamics and discrete representations to generate effective plans in complex environments. Previous studies have shown that symbolic abstractions can emerge from neural effect predictors trained with a robot's unsupervised exploration. However, these methods rely on deterministic symbolic domains, lack mechanisms to verify the generated symbolic plans, and operate only at the abstract level, often failing to capture the continuous dynamics of the environment. To overcome these limitations, we propose a bilevel neuro-symbolic framework in which learned probabilistic symbolic rules generate candidate plans rapidly at the high level, and learned continuous effect models verify these plans and perform forward search when necessary at the low level. Our experiments on multi-object manipulation tasks demonstrate that the proposed bilevel method outperforms symbolic-only approaches, reliably identifying failing plans through verification, and achieves planning performance statistically comparable to continuous forward search while resolving most problems via efficient symbolic reasoning.
Robotics (cs.RO)
arXiv:2603.08599 [cs.RO]
(or arXiv:2603.08599v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2603.08599 Focus to learn more arXiv-issued DOI via DataCite
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