V-Dreamer: Automating Robotic Simulation and Trajectory Synthesis via Video Generation Priors

• URL: http://arxiv.org/abs/2603.18811v1
• Date: Thu, 19 Mar 2026 12:00:04 GMT
• Title: V-Dreamer: Automating Robotic Simulation and Trajectory Synthesis via Video Generation Priors
• Abstract summary: V-Dreamer is a fully automated framework that generates open-vocabulary, simulation-ready manipulation environments.<n>V-Dreamer employs a novel generative pipeline that constructs physically grounded 3D scenes.<n>We show that our policies robustly generalize to unseen objects in simulation and achieve effective sim-to-real transfer.
• Score: 30.872127145725187
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Training generalist robots demands large-scale, diverse manipulation data, yet real-world collection is prohibitively expensive, and existing simulators are often constrained by fixed asset libraries and manual heuristics. To bridge this gap, we present V-Dreamer, a fully automated framework that generates open-vocabulary, simulation-ready manipulation environments and executable expert trajectories directly from natural language instructions. V-Dreamer employs a novel generative pipeline that constructs physically grounded 3D scenes using large language models and 3D generative models, validated by geometric constraints to ensure stable, collision-free layouts. Crucially, for behavior synthesis, we leverage video generation models as rich motion priors. These visual predictions are then mapped into executable robot trajectories via a robust Sim-to-Gen visual-kinematic alignment module utilizing CoTracker3 and VGGT. This pipeline supports high visual diversity and physical fidelity without manual intervention. To evaluate the generated data, we train imitation learning policies on synthesized trajectories encompassing diverse object and environment variations. Extensive evaluations on tabletop manipulation tasks using the Piper robotic arm demonstrate that our policies robustly generalize to unseen objects in simulation and achieve effective sim-to-real transfer, successfully manipulating novel real-world objects.

Related papers

• OASIS: From Simulation Data Collection to Real-World Humanoid Loco-Manipulation [51.37436817948304]
  OASIS is a simulation-data-driven framework for humanoid loco-manipulation.<n>It reconstructs realistic object assets from real-world images using a 3D generative model.<n>Under zero-shot deployment, it achieves higher success rates on most tasks than that trained on real-robot teleoperation data.
  arXiv  Detail & Related papers  (2026-06-07T10:01:53Z)
• GS-Playground: A High-Throughput Photorealistic Simulator for Vision-Informed Robot Learning [55.272458304179025]
  GS-Playground is a multi-modal simulation framework designed to accelerate end-to-end perceptual learning.<n>Our system achieves a breakthrough throughput of 104 FPS at 640x480 resolution, significantly lowering the barrier for large-scale visual RL.
  arXiv  Detail & Related papers  (2026-04-28T10:05:39Z)
• ComSim: Building Scalable Real-World Robot Data Generation via Compositional Simulation [90.4702774169675]
  We propose a novel hybrid approach called Compositional Simulation, which combines classical simulation and neural simulation.<n>Our approach utilizes a closed-loop real-sim-real data augmentation pipeline, leveraging a small amount of real-world data.<n>We train a neural simulator to transform classical simulation videos into real-world representations, improving the accuracy of policy models trained in real-world environments.
  arXiv  Detail & Related papers  (2026-04-13T12:25:45Z)
• URDF-Anything+: Autoregressive Articulated 3D Models Generation for Physical Simulation [45.4820195450296]
  We present an end-to-end autoregressive framework that directly generates executable articulated object models from visual observations.<n>Building on this capability, we enable a new Real-Follow-Sim paradigm, where high-fidelity digital twins constructed from visual observations allow policies trained in simulation to transfer to real robots without online adaptation.
  arXiv  Detail & Related papers  (2026-03-14T16:23:44Z)
• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• 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)
• AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis [33.90053396451562]
  We introduce AnchorDream, an embodiment-aware world model that repurposes pretrained video diffusion models for robot data synthesis.<n>Our method scales them into large, diverse, high-quality datasets without requiring explicit environment modeling.<n>Experiments show that the generated data leads to consistent improvements in downstream policy learning, with relative gains of 36.4% in simulator benchmarks and nearly double performance in real-world studies.
  arXiv  Detail & Related papers  (2025-12-12T18:59:45Z)
• GWM: Towards Scalable Gaussian World Models for Robotic Manipulation [53.51622803589185]
  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.
  arXiv  Detail & Related papers  (2025-08-25T02:01:09Z)
• RoboPearls: Editable Video Simulation for Robot Manipulation [81.18434338506621]
  RoboPearls is an editable video simulation framework for robotic manipulation.<n>Built on 3D Gaussian Splatting (3DGS), RoboPearls enables the construction of photo-realistic, view-consistent simulations.<n>We conduct extensive experiments on multiple datasets and scenes, including RLBench, COLOSSEUM, Ego4D, Open X-Embodiment, and a real-world robot.
  arXiv  Detail & Related papers  (2025-06-28T05:03:31Z)
• 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)
• URDFormer: A Pipeline for Constructing Articulated Simulation Environments from Real-World Images [39.0780707100513]
  We present an integrated end-to-end pipeline that generates simulation scenes complete with articulated kinematic and dynamic structures from real-world images. In doing so, our work provides both a pipeline for large-scale generation of simulation environments and an integrated system for training robust robotic control policies.
  arXiv  Detail & Related papers  (2024-05-19T20:01:29Z)

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.