DexVLG: Dexterous Vision-Language-Grasp Model at Scale

Abstract

As large models gain traction, vision-language models are enabling robots to tackle increasingly complex tasks. However, limited by the difficulty of data collection, progress has mainly focused on controlling simple gripper end-effectors. There is little research on functional grasping with large models for human-like dexterous hands. In this paper, we introduce DexVLG, a large Vision-Language-Grasp model for Dexterous grasp pose prediction aligned with language instructions using single-view RGBD input. To accomplish this, we generate a dataset of 170 million dexterous grasp poses mapped to semantic parts across 174,000 objects in simulation, paired with detailed part-level captions. This large-scale dataset, named DexGraspNet 3.0, is used to train a VLM with a flow-matching-based pose head producing instruction-aligned grasp poses for tabletop objects. To evaluate DexVLG's performance, we create benchmarks in simulations and conduct real-world experiments. Extensive experiments demonstrate DexVLG's strong zero-shot generalization capabilities, achieving an over 76% zero-shot execution success rate and state-of-the-art part-grasp accuracy in simulation, as well as successful part-aligned grasps on physical objects in real-world scenarios.