Predicting Consequences and Reinforcing Navigation Policies with Latent World Models

• URL: http://arxiv.org/abs/2608.26190v1
• Date: Sun, 23 Aug 2026 11:58:42 GMT
• Title: Predicting Consequences and Reinforcing Navigation Policies with Latent World Models
• Abstract summary: We propose a compatibility prediction Latent World Model (LWM) for robot navigation.<n>Our key insight is that spatial proximity correlates with latent feature similarity, enabling action consequences to be evaluated directly in latent space.<n>Our approach significantly outperforms prior world model and imitation learning methods in prediction accuracy, policy learning, and real-world navigation performance.
• Score: 31.865100130203317
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: World models enable agents to reason about future outcomes and learn policies from their knowledge of state transition, but existing approaches primarily focus on reconstructing future observations or features, which introduces unnecessary complexity and limits their effectiveness for decision making. In this work, we propose a compatibility prediction Latent World Model (LWM) for robot navigation that predicts action-conditioned latent feature compatibility rather than reconstructing observations. Our key insight is that spatial proximity correlates with latent feature similarity, enabling action consequences to be evaluated directly in latent space. To support counterfactual training, our model leverages action sequences sampled across trajectories and learns to predict which sequences lead closer to the goal. Furthermore, we demonstrate how the learned world model can supervise policy learning from unlabeled video data and further improve policies through reinforcement learning entirely within the world model. This imagination-driven framework eliminates the need for action annotations and additional environment interaction. Extensive experiments on multiple real-world robot navigation datasets show that our approach significantly outperforms prior world model and imitation learning methods in prediction accuracy, policy learning, and real-world navigation performance. The code, pretrained models, and additional materials are available at https://wzm206.github.io/latent-world-model-nav.

Related papers

• World-Action Models for Robot Learning and Control: A Survey [41.45329550202786]
  Recent advances in world models, video generation, and Vision-Language-Action (VLA) policies have motivated the development of World-Action Models (WAMs)<n>This survey provides a robotics-oriented review of WAMs.<n>We clarify their scope relative to conventional world models, model-based reinforcement learning, action-conditioned video generation, and reactive VLA policies, and organize existing methods through a unified taxonomy.<n>We discuss key challenges in action alignment, world-action factorization, spatial and multi-view consistency, long-horizon memory, neural simulation for closed-loop policy learning, and efficient inference
  arXiv  Detail & Related papers  (2026-09-13T16:35:56Z)
• 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)
• ExploreVLA: Dense World Modeling and Exploration for End-to-End Autonomous Driving [19.081114003415863]
  We propose a unified understanding-and-generation framework to enable meaningful exploration and provide dense supervision.<n>We augment trajectory prediction with future RGB and depth image generation as dense world modeling objectives.<n>We incorporate this exploration signal into a safety-gated reward and optimize the policy.
  arXiv  Detail & Related papers  (2026-04-03T04:14:13Z)
• LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning [51.969318585152116]
  LatentPilot exploits future observations during training as a valuable data source to learn action-conditioned visual dynamics.<n>We propose a flywheel-style training mechanism that iteratively collects on-policy trajectories and retrains the model to better match the agent's behavior distribution.<n> Experiments on R2R-CE, RxR-CE, and R2R-PE benchmarks achieve new SOTA results, and real-robot tests across diverse environments demonstrate LatentPilot's superior understanding of environment-action dynamics in scene.
  arXiv  Detail & Related papers  (2026-03-31T02:21:59Z)
• World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy [55.03832008486675]
  World-VLA-Loop is a closed-loop framework for the joint refinement of world models and Vision-Language-Action (VLA) policies.<n>We propose a state-aware video world model that functions as a high-fidelity interactive simulator by jointly predicting future observations and reward signals.
  arXiv  Detail & Related papers  (2026-02-06T08:57:55Z)
• Learning Generalizable Visuomotor Policy through Dynamics-Alignment [13.655111993491674]
  Recent approaches leveraging video prediction models have shown promising results by learning rich representations from large-scale datasets.<n>We propose a Dynamics-Aligned Flow Matching Policy (DAP) that integrates dynamics prediction into policy learning.<n>Our method introduces a novel architecture where policy and dynamics models provide mutual corrective feedback during action generation, enabling self-correction and improved generalization.
  arXiv  Detail & Related papers  (2025-10-31T02:29:33Z)
• From Seeing to Experiencing: Scaling Navigation Foundation Models with Reinforcement Learning [59.88543114325153]
  We introduce the Seeing-to-Experiencing framework to scale the capability of navigation foundation models with reinforcement learning.<n>S2E combines the strengths of pre-training on videos and post-training through RL.<n>We establish a comprehensive end-to-end evaluation benchmark, NavBench-GS, built on photorealistic 3DGS reconstructions of real-world scenes.
  arXiv  Detail & Related papers  (2025-07-29T17:26:10Z)
• Learning World Models for Unconstrained Goal Navigation [4.549550797148707]
  We introduce a goal-directed exploration algorithm, MUN, for learning world models. MUN is capable of modeling state transitions between arbitrary subgoal states in the replay buffer. Results demonstrate that MUN strengthens the reliability of world models and significantly improves the policy's capacity to generalize.
  arXiv  Detail & Related papers  (2024-11-03T01:35:06Z)
• Multi-Agent Dynamic Relational Reasoning for Social Robot Navigation [50.01551945190676]
  Social robot navigation can be helpful in various contexts of daily life but requires safe human-robot interactions and efficient trajectory planning. We propose a systematic relational reasoning approach with explicit inference of the underlying dynamically evolving relational structures. We demonstrate its effectiveness for multi-agent trajectory prediction and social robot navigation.
  arXiv  Detail & Related papers  (2024-01-22T18:58:22Z)
• 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)
• Predictive World Models from Real-World Partial Observations [66.80340484148931]
  We present a framework for learning a probabilistic predictive world model for real-world road environments. While prior methods require complete states as ground truth for learning, we present a novel sequential training method to allow HVAEs to learn to predict complete states from partially observed states only.
  arXiv  Detail & Related papers  (2023-01-12T02:07:26Z)
• Dream to Explore: Adaptive Simulations for Autonomous Systems [3.0664963196464448]
  We tackle the problem of learning to control dynamical systems by applying Bayesian nonparametric methods. By employing Gaussian processes to discover latent world dynamics, we mitigate common data efficiency issues observed in reinforcement learning. Our algorithm jointly learns a world model and policy by optimizing a variational lower bound of a log-likelihood.
  arXiv  Detail & Related papers  (2021-10-27T04:27:28Z)
• 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.