GraspGen: オブジェクト把持のための拡散モデルフレームワーク
GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
拡散モデルを用いて6自由度の把持を生成し、識別器でフィルタリングするフレームワークを提案。5300万把持のデータセットを公開し、実機でも高い性能を示した。
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2. 先行研究と比べてどこがすごい？
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著者: Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao, Wentao Yuan, Jun Yamada, Mark Carlson, Fabio Ramos, Stan Birchfield, Dieter Fox, Clemens Eppner
分類: cs.RO, cs.AI
原文アブストラクト
Grasping is a fundamental robot skill, yet despite significant research advancements, learning-based 6-DOF grasping approaches are still not turnkey and struggle to generalize across different embodiments and in-the-wild settings. We build upon the recent success on modeling the object-centric grasp generation process as an iterative diffusion process. Our proposed framework, GraspGen, consists of a DiffusionTransformer architecture that enhances grasp generation, paired with an efficient discriminator to score and filter sampled grasps. We introduce a novel and performant on-generator training recipe for the discriminator. To scale GraspGen to both objects and grippers, we release a new simulated dataset consisting of over 53 million grasps. We demonstrate that GraspGen outperforms prior methods in simulations with singulated objects across different grippers, achieves state-of-the-art performance on the FetchBench grasping benchmark, and performs well on a real robot with noisy visual observations.
