Bilevel Planning with Learned Symbolic Abstractions from Interaction Data

• URL: http://arxiv.org/abs/2603.08599v1
• Date: Mon, 09 Mar 2026 16:47:03 GMT
• Title: Bilevel Planning with Learned Symbolic Abstractions from Interaction Data
• Abstract summary: We propose a bilevel neuro-symbolic framework in which learned probabilistic symbolic rules generate candidate plans.<n>Experiments on multi-object manipulation tasks demonstrate that the proposed bilevel method outperforms symbolic-only approaches.
• Score: 1.5772749239784327
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Neuro-Symbolic Learning for Long-Horizon Task Planning Under Complex Logical Constraints [12.864903019173106]
  Recent neuro-symbolic methods improve planning efficiency by learning object-importance scores to prune task-irrelevant objects.<n>We introduce a 3R strategy into the lower-level planning, using parallel Repair, Restart, and Rollback recovery to provide reliable and adaptive feedback for upper-level learning.<n>Experiments on three challenging benchmarks demonstrate state-of-the-art performance, including an 80.04% reduction in failure rate and a 57.14% reduction in planning time.
  arXiv  Detail & Related papers  (2026-06-05T03:44:48Z)
• Emergent Neural Automaton Policies: Learning Symbolic Structure from Visuomotor Trajectories [14.475289544849007]
  ENAP (Emergent Neural Automaton Policy) is a framework that allows a bi-level neuro-symbolic policy adaptively emerge from visuomotor demonstrations.<n>By explicitly modeling the task structure with discrete transitions and continuous residuals, ENAP achieves high sample efficiency and interpretability without requiring task-specific labels.
  arXiv  Detail & Related papers  (2026-03-26T20:50:36Z)
• Discrete JEPA: Learning Discrete Token Representations without Reconstruction [23.6286989806018]
  Symbolic cornerstone of cognitive intelligence lies in extracting hidden patterns from observations.<n>We propose Discrete-JEPA, extending latent predictive coding framework with semantic tokenization.<n>Our approach promises a significant impact for advancing world modeling and planning capabilities in artificial intelligence systems.
  arXiv  Detail & Related papers  (2025-06-17T10:15:17Z)
• Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning [51.54559117314768]
  Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of guidance to improve the performance of temporal planners.<n>We propose an evolution of this learning and planning framework that focuses on exploiting the information provided by symbolics during both the RL and planning phases.
  arXiv  Detail & Related papers  (2025-05-19T17:19:13Z)
• Hierarchical Neuro-Symbolic Decision Transformer [6.4149117677272525]
  We present a hierarchical neuro-symbolic control framework that tightly couples a classical symbolic planner with a transformer-based policy.<n>At the high level, the planner assembles an interpretable sequence of operators that guarantees logical coherence with task constraints.<n>At the low level each operator is rendered as a sub-goal token that conditions a decision transformer to generate fine-grained actions.
  arXiv  Detail & Related papers  (2025-03-10T10:22:13Z)
• Advanced Weakly-Supervised Formula Exploration for Neuro-Symbolic Mathematical Reasoning [18.937801725778538]
  We propose an advanced practice for neuro-symbolic reasoning systems to explore the intermediate labels with weak supervision from problem inputs and final outputs.<n>Our experiments on the Mathematics dataset illustrated the effectiveness of our proposals from multiple aspects.
  arXiv  Detail & Related papers  (2025-02-02T02:34:36Z)
• VisualPredicator: Learning Abstract World Models with Neuro-Symbolic Predicates for Robot Planning [86.59849798539312]
  We present Neuro-Symbolic Predicates, a first-order abstraction language that combines the strengths of symbolic and neural knowledge representations.<n>We show that our approach offers better sample complexity, stronger out-of-distribution generalization, and improved interpretability.
  arXiv  Detail & Related papers  (2024-10-30T16:11:05Z)
• Unified Task and Motion Planning using Object-centric Abstractions of Motion Constraints [56.283944756315066]
  We propose an alternative TAMP approach that unifies task and motion planning into a single search. Our approach is based on an object-centric abstraction of motion constraints that permits leveraging the computational efficiency of off-the-shelf AI search to yield physically feasible plans.
  arXiv  Detail & Related papers  (2023-12-29T14:00:20Z)
• Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
  We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT) Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives. This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
  arXiv  Detail & Related papers  (2023-10-03T05:40:56Z)
• Hierarchical Imitation Learning with Vector Quantized Models [77.67190661002691]
  We propose to use reinforcement learning to identify subgoals in expert trajectories. We build a vector-quantized generative model for the identified subgoals to perform subgoal-level planning. In experiments, the algorithm excels at solving complex, long-horizon decision-making problems outperforming state-of-the-art.
  arXiv  Detail & Related papers  (2023-01-30T15:04:39Z)
• Symbolic Visual Reinforcement Learning: A Scalable Framework with Object-Level Abstraction and Differentiable Expression Search [63.3745291252038]
  We propose DiffSES, a novel symbolic learning approach that discovers discrete symbolic policies. By using object-level abstractions instead of raw pixel-level inputs, DiffSES is able to leverage the simplicity and scalability advantages of symbolic expressions. Our experiments demonstrate that DiffSES is able to generate symbolic policies that are simpler and more scalable than state-of-the-art symbolic RL methods.
  arXiv  Detail & Related papers  (2022-12-30T17:50:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.