MWM: Mobile World Models for Action-Conditioned Consistent Prediction

• URL: http://arxiv.org/abs/2603.07799v1
• Date: Sun, 08 Mar 2026 20:54:50 GMT
• Title: MWM: Mobile World Models for Action-Conditioned Consistent Prediction
• Abstract summary: We propose a mobile world model for planning-based image-goal navigation.<n>We introduce a two-stage training framework that combines structure pretraining with Action-Conditioned Consistency (ACC) post-training to improve rollout consistency.<n>Our experiments on benchmark and real-world tasks demonstrate consistent gains in visual fidelity, trajectory accuracy, planning success, and inference efficiency.
• Score: 19.819967401664464
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: World models enable planning in imagined future predicted space, offering a promising framework for embodied navigation. However, existing navigation world models often lack action-conditioned consistency, so visually plausible predictions can still drift under multi-step rollout and degrade planning. Moreover, efficient deployment requires few-step diffusion inference, but existing distillation methods do not explicitly preserve rollout consistency, creating a training-inference mismatch. To address these challenges, we propose MWM, a mobile world model for planning-based image-goal navigation. Specifically, we introduce a two-stage training framework that combines structure pretraining with Action-Conditioned Consistency (ACC) post-training to improve action-conditioned rollout consistency. We further introduce Inference-Consistent State Distillation (ICSD) for few-step diffusion distillation with improved rollout consistency. Our experiments on benchmark and real-world tasks demonstrate consistent gains in visual fidelity, trajectory accuracy, planning success, and inference efficiency. Code: https://github.com/AIGeeksGroup/MWM. Website: https://aigeeksgroup.github.io/MWM.

Related papers

• ABot-M0.5: Unified Mobility-and-Manipulation World Action Model [27.262271208923995]
  ABot-M0.5 is built on the insight that mobile manipulation requires alignment at three levels: temporal granularity, action space, and train-test consistency.<n> Experiments on challenging mobile and fine-grained manipulation benchmarks demonstrate that ABot-M0.5 achieves state-of-the-art performance in both long-horizon task success and finegrained control accuracy.
  arXiv  Detail & Related papers  (2026-07-01T09:21:20Z)
• Sensorimotor World Models: Perception for Action via Inverse Dynamics [59.121736411156384]
  We introduce a sensorimotor world model trained end-to-end with inverse dynamics regularization.<n>It prevents representation collapse and induces action-aligned representations.<n>It learns compact, interpretable latent spaces and enables competitive planning performance across simple 2D and 3D control tasks.
  arXiv  Detail & Related papers  (2026-06-18T11:25:16Z)
• Metis: A Generalizable and Efficient World-Action Model for Autonomous Driving and Urban Navigation [80.18527639791074]
  World action models have shown great promise for autonomous driving and urban navigation.<n>Existing approaches suffer key limitations.<n>We propose Metis, an end-to-end WAM framework that decouples video generation and action prediction.
  arXiv  Detail & Related papers  (2026-06-14T15:40:49Z)
• HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models [58.191567345416836]
  World Action Models (WAMs) have emerged as a promising paradigm for robot control by modeling physical dynamics.<n>HarmoWAM fully leverages a world model to unify predictive and reactive control, enabling both general transit and precise manipulation.<n>We construct three training-unseen test environments across six real-world robotic tasks, covering variations in background, position, and object semantics.
  arXiv  Detail & Related papers  (2026-05-11T17:59:56Z)
• Learning Lifted Action Models from Unsupervised Visual Traces [38.479081137300945]
  We propose a deep learning framework that jointly learns state prediction, action prediction, and a lifted action model.<n>We also introduce a mixed-integer linear program (MILP) to prevent prediction collapse and self-reinforcing errors.<n>Experiments across multiple domains show that integrating MILP-based correction helps the model escape local optima and converge toward globally consistent solutions.
  arXiv  Detail & Related papers  (2026-04-21T03:49:04Z)
• DDP-WM: Disentangled Dynamics Prediction for Efficient World Models [79.53092337527382]
  We introduce DDP-WM, a novel world model centered on the principle of Disentangled Dynamics Prediction.<n>DDP-WM realizes this decomposition through an architecture that integrates efficient historical processing with dynamic localization.<n>Experiments demonstrate that DDP-WM achieves significant efficiency and performance across diverse tasks.
  arXiv  Detail & Related papers  (2026-02-02T08:04:25Z)
• AstraNav-World: World Model for Foresight Control and Consistency [40.07910402326578]
  Embodied navigation in dynamic environments demands accurate foresight of how the world will evolve and how actions will unfold over time.<n>We propose AstraNav-World, an end-to-end world model that jointly reasons about future visual states and action sequences.<n>Our framework integrates a diffusion-based video generator with a vision-language policy, enabling synchronized rollouts.
  arXiv  Detail & Related papers  (2025-12-25T15:31:24Z)
• Dual-Stream Diffusion for World-Model Augmented Vision-Language-Action Model [62.889356203346985]
  We propose DUal-STream diffusion (DUST), a world-model augmented VLA framework that handles the modality conflict.<n>DUST achieves up to 6% gains over a standard VLA baseline and implicit world-modeling methods.<n>On real-world tasks with the Franka Research 3, DUST outperforms baselines in success rate by 13%.
  arXiv  Detail & Related papers  (2025-10-31T16:32:12Z)
• From Forecasting to Planning: Policy World Model for Collaborative State-Action Prediction [57.56072009935036]
  We introduce a new driving paradigm named Policy World Model (PWM)<n>PWM integrates world modeling and trajectory planning within a unified architecture.<n>Our method matches or exceeds state-of-the-art approaches that rely on multi-view and multi-modal inputs.
  arXiv  Detail & Related papers  (2025-10-22T14:57:51Z)
• Unified World Models: Memory-Augmented Planning and Foresight for Visual Navigation [69.94565127141483]
  Current approaches separate navigation planning from visual world modeling, leading to state-action misalignment and limited adaptability.<n>We propose UniWM, a unified, memory-augmented world model integrating egocentric visual foresight and planning within a single multimodal autoregressive backbone.<n>We show that UniWM substantially improves navigation success rates by up to 30%, significantly reduces trajectory errors compared to strong baselines, and exhibits impressive zero-shot generalization on the unseen TartanDrive dataset.
  arXiv  Detail & Related papers  (2025-10-09T18:18:11Z)
• MinD: Learning A Dual-System World Model for Real-Time Planning and Implicit Risk Analysis [32.08769443927576]
  We propose Manipulate in Dream (MinD), a dual-system world model for real-time, risk-aware planning.<n>MinD uses two asynchronous diffusion processes: a low-frequency visual generator (LoDiff) that predicts future scenes and a high-frequency diffusion policy (HiDiff) that outputs actions.<n>Our key insight is that robotic policies do not require fully denoised frames but can rely on low-resolution latents generated in a single denoising step.<n>MinD achieves a 63% success rate on RL-Bench, 60% on real-world Franka tasks, and operates at 11.3 FPS
  arXiv  Detail & Related papers  (2025-06-23T17:59:06Z)
• Predictive Planner for Autonomous Driving with Consistency Models [5.966385886363771]
  Trajectory prediction and planning are essential for autonomous vehicles to navigate safely and efficiently in dynamic environments.<n>Recent diffusion-based generative models have shown promise in multi-agent trajectory generation, but their slow sampling is less suitable for high-frequency planning tasks.<n>We leverage the consistency model to build a predictive planner that samples from a joint distribution of ego and surrounding agents, conditioned on the ego vehicle's navigational goal.
  arXiv  Detail & Related papers  (2025-02-12T00:26:01Z)
• Bootstrap Motion Forecasting With Self-Consistent Constraints [52.88100002373369]
  We present a novel framework to bootstrap Motion forecasting with Self-consistent Constraints. The motion forecasting task aims at predicting future trajectories of vehicles by incorporating spatial and temporal information from the past. We show that our proposed scheme consistently improves the prediction performance of several existing methods.
  arXiv  Detail & Related papers  (2022-04-12T14:59:48Z)

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.