GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training

• URL: http://arxiv.org/abs/2507.13097v1
• Date: Thu, 17 Jul 2025 13:09:28 GMT
• Title: GraspGen: A Diffusion-based Framework for 6-DOF Grasping with On-Generator Training
• Authors: Adithyavairavan Murali, Balakumar Sundaralingam, Yu-Wei Chao, Wentao Yuan, Jun Yamada, Mark Carlson, Fabio Ramos, Stan Birchfield, Dieter Fox, Clemens Eppner,
• Abstract summary: We build upon the recent success on modeling the object-centric grasp generation process as an iterative diffusion process.<n>Our proposed framework, GraspGen, consists of a DiffusionTransformer architecture that enhances grasp generation, paired with an efficient discriminator to score and filter sampled grasps.<n>To scale GraspGen to both objects and grippers, we release a new simulated dataset consisting of over 53 million grasps.
• Score: 53.25060512131128
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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