DexDiffuser: Generating Dexterous Grasps with Diffusion Models
Zehang Weng Haofei Lu Danica Kragic Jens Lundell
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).
Our simulation and real-world experiments on the Allegro Hand consistently
demonstrate that DexDiffuser outperforms the state-of-the-art multi-finger
grasp generation method FFHNet with an, on average, 21.71--22.20\% higher grasp
success rate.