Towards Zero-Shot Task Transfer with Neurosymbolic World Models

• URL: http://arxiv.org/abs/2608.17959v2
• Date: Mon, 24 Aug 2026 12:25:21 GMT
• Title: Towards Zero-Shot Task Transfer with Neurosymbolic World Models
• Abstract summary: State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space.<n>We present a novel world model formulation where the reward prediction only depends on a subset of structured, symbolic components of the whole latent state.<n>We discuss the main advantages and challenges of learning these neurosymbolic world models and demonstrate the strong generalisation properties of our approach over purely neural methods.
• Score: 17.135921836577715
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: State-of-the-art model-based reinforcement learning methods learn neural world models that allow policy improvement by planning in a latent space, without assumptions on the structure of the underlying environment. While expressive, these models are generally task-dependent: they learn uninterpretable latent representations that are tied to the training task and thus hard to generalize to new tasks. In this work, we present a novel world model formulation where the reward prediction only depends on a subset of structured, symbolic components of the whole latent state. Decoupling observation reconstruction and reward prediction allows us to learn world models that can adapt zero-shot, i.e. without further environment interactions, to new reward functions defined over the same symbolic state space. We discuss the main advantages and challenges of learning these neurosymbolic world models and demonstrate the strong generalisation properties of our approach over purely neural methods.

Related papers

• Towards Predictive, Aligned, and Scalable Robot Learning [11.830982964522605]
  We introduce Lumo-2, a latent world-action model that generates actions by reasoning over world dynamics in latent space.<n>We study latent world modelling and modality alignment, analyzing their roles in scaling laws and out-of-distribution generalization.<n>Results show that Lumo-2 consistently outperforms strong vision-language-action (VLA) and world-action model (WAM) baselines.
  arXiv  Detail & Related papers  (2026-07-13T08:53:34Z)
• From World Models to World Action Models: A Concise Tutorial for Robotics [3.9330868250536457]
  This tutorial presents a design-space view of world models as action-conditioned predictive models.<n>The goal of this tutorial is to clarify the conceptual scope of world (action) models and provide a structured taxonomy for embodied prediction and control.
  arXiv  Detail & Related papers  (2026-07-01T11:56:54Z)
• World Action Models: The Next Frontier in Embodied AI [123.5787299299832]
  Vision-Language-Action (VLA) models have achieved strong semantic generalization for embodied policy learning.<n>They learn reactive observation-to-action mappings without explicitly modeling how the physical world evolves under intervention.<n>A growing body of work addresses this limitation by integrating world models, predictive models of environment dynamics, into the action generation pipeline.
  arXiv  Detail & Related papers  (2026-05-12T13:10:52Z)
• SmallWorlds: Assessing Dynamics Understanding of World Models in Isolated Environments [15.243547292947397]
  We introduce the SmallWorld Benchmark, a testbed designed to assess world model capability under isolated and precisely controlled dynamics.<n>We conduct comprehensive experiments in the fully observable state space on representative architectures including Recurrent State Space Model, Transformer, Diffusion model, and Neural ODE.<n>The experimental results reveal how effectively these models capture environment structure and how their predictions deteriorate over extended rollouts.
  arXiv  Detail & Related papers  (2025-11-28T18:56:02Z)
• Adaptive World Models: Learning Behaviors by Latent Imagination Under Non-Stationarity [16.15952351162363]
  We introduce a new formalism, Hidden. POMDP, designed for control with adaptive world models. We demonstrate that this approach enables learning robust behaviors across a variety of non-stationary RL benchmarks.
  arXiv  Detail & Related papers  (2024-11-02T19:09:56Z)
• 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)
• A Novel Neural-symbolic System under Statistical Relational Learning [47.30190559449236]
  We propose a neural-symbolic framework based on statistical relational learning, referred to as NSF-SRL.<n>Results of symbolic reasoning are utilized to refine and correct the predictions made by deep learning models, while deep learning models enhance the efficiency of the symbolic reasoning process.<n>We believe that this approach sets a new standard for neural-symbolic systems and will drive future research in the field of general artificial intelligence.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Continual Visual Reinforcement Learning with A Life-Long World Model [55.05017177980985]
  We present a new continual learning approach for visual dynamics modeling.<n>We first introduce the life-long world model, which learns task-specific latent dynamics.<n>Then, we address the value estimation challenge for previous tasks with the exploratory-conservative behavior learning approach.
  arXiv  Detail & Related papers  (2023-03-12T05:08:03Z)
• Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning [50.96320003643406]
  Domain-general model-based planners often derive their generality by constructing searchs through the relaxation of symbolic world models. We illustrate how abstract interpretation can serve as a unifying framework for these abstractions, extending the reach of search to richer world models. Theses can also be integrated with learning, allowing agents to jumpstart planning in novel world models via abstraction-derived information.
  arXiv  Detail & Related papers  (2022-08-05T00:22:11Z)
• Towards a Predictive Processing Implementation of the Common Model of Cognition [79.63867412771461]
  We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory. The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
  arXiv  Detail & Related papers  (2021-05-15T22:55:23Z)
• Goal-Aware Prediction: Learning to Model What Matters [105.43098326577434]
  One of the fundamental challenges in using a learned forward dynamics model is the mismatch between the objective of the learned model and that of the downstream planner or policy. We propose to direct prediction towards task relevant information, enabling the model to be aware of the current task and encouraging it to only model relevant quantities of the state space. We find that our method more effectively models the relevant parts of the scene conditioned on the goal, and as a result outperforms standard task-agnostic dynamics models and model-free reinforcement learning.
  arXiv  Detail & Related papers  (2020-07-14T16:42:59Z)

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.