V-Dreamer: ビデオ生成事前知識によるロボットシミュレーションと軌道合成の自動化
V-Dreamer: Automating Robotic Simulation and Trajectory Synthesis via Video Generation Priors
自然言語の指示から、シミュレーション可能な3D環境と実行可能なロボット軌道を自動生成するフレームワークを提案。ビデオ生成モデルを動作の事前知識として活用し、シミュレーションから実世界への転移を実現した。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
5. 議論はある？
6. 次に読むべき論文は？
※ AIが要旨から生成した要約です。正確性は原文をご確認ください。
著者: Songjia He, Zixuan Chen, Hongyu Ding, Dian Shao, Jieqi Shi, Chenxu Li, Jing Huo, Yang Gao
分類: cs.RO
原文アブストラクト
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.
関連論文
- MMUSV-Sim: 多USV協調認識のための認識指向シミュレーション・データ生成プラットフォームシミュレーション/データ生成
