PhyVisGen: 物理的・視覚的に高忠実なロボットマニピュレーションデータ生成
PhyVisGen: Physically and Visually High-Fidelity Robotic Manipulation Data Generation
ソフトグリッパを含む操作軌跡の物理シミュレーションと実シーン再構成によるリアルな描画を組み合わせ、実機データなしで学習した方策が実ロボット5タスクで65〜95%の成功率を達成したデータ生成フレームワーク。
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: Yu Zheng, Qiyu Feng, Yixin Wu, Baoquan Yang, Yixuan Zhou, Bingyang Hu, Kemeng Huang, Guansheng Yang, Hesheng Wang
分類: cs.RO
原文アブストラクト
Large-scale manipulation demonstrations are essential for learning robust visuomotor policies, yet real-world data collection is expensive and difficult to scale. Simulation offers a promising alternative, but physical and visual discrepancies can limit the transferability of synthetic data, particularly for manipulation with soft grippers. We present PhyVisGen, a physically and visually high-fidelity framework for scalable robotic manipulation data generation. On the physical side, PhyVisGen introduces an arm-gripper coupling method based on the Incremental Potential Contact (IPC), enabling high-fidelity soft contact throughout complete manipulation trajectories. On the visual side, it combines real-scene reconstruction with real-time path tracing to generate visually realistic observations while preserving captured scene appearance. Quantitative evaluations demonstrate the physical and visual fidelity of PhyVisGen. Policies trained exclusively on synthetic manipulation demonstrations achieve 65-95% success across five real-robot tasks, without real-robot demonstration data or policy fine-tuning.