ColaDex: Contact-guided Optimization and VLM-assisted Selection for Task-oriented Dexterous Grasp Generation

Abstract

Task-oriented dexterous grasp generation aims to generate stable and functional grasps that enable a robotic hand to effectively interact with objects to accomplish specific tasks. However, generating high-dimensional hand configurations that seamlessly adapt to diverse task requirements and object geometries remains a significant challenge. In this paper, we propose a novel pipeline called ColaDex to address this challenging problem. The core idea of ColaDex is to leverage a vision-language models (VLMs) to select the dexterous grasp from a set of candidates that aligns well with the task description. To this end, we first introduce a contact-guided optimization method to generate a set of high-quality grasp candidates around the object through analytical optimization. Subsequently, to effectively prompt VLMs with the sampled numerous grasp candidates, we propose an object-centric approach that adaptively represents a group of candidates as prototypical contact maps, learned based on the geometric relationships between the grasping hand and object shape. We then feed the task requirement and the generated prototypical contact maps into the VLM, enabling it to reason about grasp-object interactions and assess their alignment with the given task, ultimately selecting the grasp that best aligns with the task requirement. Extensive experiments demonstrate that our prototypical contact map is a more informative prompting mechanism than conventional RGB images, enabling ColaDex to consistently generate high-quality task-oriented grasps and achieve a high success rate across diverse objects and tasks.