DexDiffuser: Generating Dexterous Grasps with Diffusion Models
<|reference_start|>DexDiffuser: Generating Dexterous Grasps with Diffusion Models: We introduce DexDiffuser, a novel dexterous grasping method that generates, evaluates, and refines grasps on partial object point clouds. DexDiffuser includes the conditional diffusion-based grasp sampler DexSampler and the dexterous grasp evaluator DexEvaluator. DexSampler generates high-quality grasps conditioned on object point clouds by iterative denoising of randomly sampled grasps. We also introduce two grasp refinement strategies: Evaluator-Guided Diffusion (EGD) and Evaluator-based Sampling Refinement (ESR). The experiment results demonstrate that DexDiffuser consistently outperforms the state-of-the-art multi-finger grasp generation method FFHNet with an, on average, 9.12% and 19.44% higher grasp success rate in simulation and real robot experiments, respectively. Supplementary materials are available at https://yulihn.github.io/DexDiffuser_page/<|reference_end|>
@article{weng2024dexdiffuser:,
    title={DexDiffuser: Generating Dexterous Grasps with Diffusion Models},
    author={Zehang Weng, Haofei Lu, Danica Kragic, Jens Lundell},
    journal={arXiv preprint arXiv:2402.02989},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.02989},
    primaryClass={cs.RO cs.LG}
}