GWM: Towards Scalable Gaussian World Models for Robotic Manipulation

• URL: http://arxiv.org/abs/2508.17600v2
• Date: Wed, 17 Sep 2025 02:27:49 GMT
• Title: GWM: Towards Scalable Gaussian World Models for Robotic Manipulation
• Authors: Guanxing Lu, Baoxiong Jia, Puhao Li, Yixin Chen, Ziwei Wang, Yansong Tang, Siyuan Huang,
• Abstract summary: We propose a novel branch of world model named Gaussian World Model (GWM) for robotic manipulation.<n>At its core is a latent Diffusion Transformer (DiT) combined with a 3D variational autoencoder, enabling fine-grained scene-level future state reconstruction.<n>Both simulated and real-world experiments depict that GWM can precisely predict future scenes conditioned on diverse robot actions.
• Score: 53.51622803589185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training robot policies within a learned world model is trending due to the inefficiency of real-world interactions. The established image-based world models and policies have shown prior success, but lack robust geometric information that requires consistent spatial and physical understanding of the three-dimensional world, even pre-trained on internet-scale video sources. To this end, we propose a novel branch of world model named Gaussian World Model (GWM) for robotic manipulation, which reconstructs the future state by inferring the propagation of Gaussian primitives under the effect of robot actions. At its core is a latent Diffusion Transformer (DiT) combined with a 3D variational autoencoder, enabling fine-grained scene-level future state reconstruction with Gaussian Splatting. GWM can not only enhance the visual representation for imitation learning agent by self-supervised future prediction training, but can serve as a neural simulator that supports model-based reinforcement learning. Both simulated and real-world experiments depict that GWM can precisely predict future scenes conditioned on diverse robot actions, and can be further utilized to train policies that outperform the state-of-the-art by impressive margins, showcasing the initial data scaling potential of 3D world model.

Related papers

• Learning Humanoid End-Effector Control for Open-Vocabulary Visual Loco-Manipulation [14.013652439013692]
  This paper presents a new paradigm, HERO, for object loco-manipulation with humanoid robots.<n>We achieve this by designing an accurate residual-aware EE tracking policy.<n>We use this accurate end-effector tracker to build a modular system for loco-manipulation.
  arXiv  Detail & Related papers  (2026-02-18T18:55:02Z)
• GeneralVLA: Generalizable Vision-Language-Action Models with Knowledge-Guided Trajectory Planning [20.646039344274556]
  GeneralVLA is a hierarchical vision-language-action (VLA) model that can be more effective in utilizing the generalization of foundation models.<n>GeneralVLA successfully generates trajectories for 14 tasks, significantly outperforming state-of-the-art methods such as VoxPoser.
  arXiv  Detail & Related papers  (2026-02-04T08:30:27Z)
• Mirage2Matter: A Physically Grounded Gaussian World Model from Video [87.9732484393686]
  We present Simulate Anything, a graphics-driven world modeling and simulation framework.<n>Our approach reconstructs real-world environments into a photorealistic scene representation using 3D Gaussian Splatting (3DGS)<n>We then leverage generative models to recover a physically realistic representation and integrate it into a simulation environment via a precision calibration target.
  arXiv  Detail & Related papers  (2026-01-24T07:43:57Z)
• Generalizable Geometric Prior and Recurrent Spiking Feature Learning for Humanoid Robot Manipulation [90.90219129619344]
  This paper presents a novel R-prior-S, Recurrent Geometric-priormodal Policy with Spiking features.<n>To ground high-level reasoning in physical reality, we leverage lightweight 2D geometric inductive biases.<n>For the data efficiency issue in robotic action generation, we introduce a Recursive Adaptive Spiking Network.
  arXiv  Detail & Related papers  (2026-01-13T23:36:30Z)
• DynaRend: Learning 3D Dynamics via Masked Future Rendering for Robotic Manipulation [52.136378691610524]
  We present DynaRend, a representation learning framework that learns 3D-aware and dynamics-informed triplane features.<n>By pretraining on multi-view RGB-D video data, DynaRend jointly captures spatial geometry, future dynamics, and task semantics in a unified triplane representation.<n>We evaluate DynaRend on two challenging benchmarks, RLBench and Colosseum, demonstrating substantial improvements in policy success rate, generalization to environmental perturbations, and real-world applicability across diverse manipulation tasks.
  arXiv  Detail & Related papers  (2025-10-28T10:17:11Z)
• DreamGen: Unlocking Generalization in Robot Learning through Video World Models [120.25799361925387]
  DreamGen is a pipeline for training robot policies that generalize across behaviors and environments through neural trajectories.<n>Our work establishes a promising new axis for scaling robot learning well beyond manual data collection.
  arXiv  Detail & Related papers  (2025-05-19T04:55:39Z)
• Pre-training Auto-regressive Robotic Models with 4D Representations [43.80798244473759]
  ARM4R is an Auto-regressive Robotic Model that leverages low-level 4D Representations learned from human video data to yield a better pre-trained robotic model.<n>Our experiments show that ARM4R can transfer efficiently from human video data to robotics and consistently improves performance on tasks across various robot environments and configurations.
  arXiv  Detail & Related papers  (2025-02-18T18:59:01Z)
• Pre-Trained Video Generative Models as World Simulators [59.546627730477454]
  We propose Dynamic World Simulation (DWS) to transform pre-trained video generative models into controllable world simulators.<n>To achieve precise alignment between conditioned actions and generated visual changes, we introduce a lightweight, universal action-conditioned module.<n> Experiments demonstrate that DWS can be versatilely applied to both diffusion and autoregressive transformer models.
  arXiv  Detail & Related papers  (2025-02-10T14:49:09Z)
• Dream to Manipulate: Compositional World Models Empowering Robot Imitation Learning with Imagination [25.62602420895531]
  DreMa is a new approach for constructing digital twins using learned explicit representations of the real world and its dynamics.<n>We show that DreMa can successfully learn novel physical tasks from just a single example per task variation.
  arXiv  Detail & Related papers  (2024-12-19T15:38:15Z)
• Dynamic 3D Gaussian Tracking for Graph-Based Neural Dynamics Modeling [10.247075501610492]
  We introduce a framework to learn object dynamics directly from multi-view RGB videos. We train a particle-based dynamics model using Graph Neural Networks. Our method can predict object motions under varying initial configurations and unseen robot actions.
  arXiv  Detail & Related papers  (2024-10-24T17:02:52Z)
• IRASim: A Fine-Grained World Model for Robot Manipulation [24.591694756757278]
  We present IRASim, a novel world model capable of generating videos with fine-grained robot-object interaction details.<n>We train a diffusion transformer and introduce a novel frame-level action-conditioning module within each transformer block to explicitly model and strengthen the action-frame alignment.
  arXiv  Detail & Related papers  (2024-06-20T17:50:16Z)
• Real-World Humanoid Locomotion with Reinforcement Learning [92.85934954371099]
  We present a fully learning-based approach for real-world humanoid locomotion. Our controller can walk over various outdoor terrains, is robust to external disturbances, and can adapt in context.
  arXiv  Detail & Related papers  (2023-03-06T18:59:09Z)
• T3VIP: Transformation-based 3D Video Prediction [49.178585201673364]
  We propose a 3D video prediction (T3VIP) approach that explicitly models the 3D motion by decomposing a scene into its object parts. Our model is fully unsupervised, captures the nature of the real world, and the observational cues in image and point cloud domains constitute its learning signals. To the best of our knowledge, our model is the first generative model that provides an RGB-D video prediction of the future for a static camera.
  arXiv  Detail & Related papers  (2022-09-19T15:01:09Z)

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.