GraspGen-X: クロスエンボディメント6自由度拡散ベース把持
GraspGen-X: Cross-Embodiment 6-DOF Diffusion-based Grasping
異なるグリッパー形状や物理的把持プロセスにも対応できる、拡散モデルを用いた6自由度把持生成手法を提案。スイープボリュームによるグリッパー表現と手続き的グリッパーによる大規模データセットで訓練し、未知のグリッパーへのゼロショット汎化を実現した。
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？
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著者: Beining Han, Yu-Wei Chao, Erwin Coumans, Clemens Eppner, Balakumar Sundaralingam, Jia Deng, Stan Birchfield, Adithyavairavan Murali
分類: cs.RO
原文アブストラクト
We study cross-embodiment 6-DOF robot grasping. Unlike prior works, we require the model not only to generalize to novel objects / scenes but also to novel gripper morphologies and physical grasping processes. Our method extends diffusion model based generative 6-DOF grasping models to condition on the additional gripper's representation. We propose a swept-volume heuristic for encoding the gripper. We train our cross-embodiment model with procedural grippers and a large-scale dataset of 2 Billion grasps. In simulation experiments, our model has the best zero-shot generalization to novel real-world grippers and objects over baseline methods. Our model also serves as a good initialization for fine-tuning to adapt to novel grippers. In ablations, we demonstrate the efficiency of our sweep-volume gripper representation and our procedural gripper training dataset. Last, we show zero-shot generalization to real-world novel grippers for 6-DOF grasping, surpassing baselines in cross-embodiment generalization.
