LDexMM: Language-Guided Dexterous Multi-Task Manipulation with Reinforcement Learning

Abstract

Language plays a crucial role in robotic manipulation, particularly in facilitating complex tasks. Previous work primarily focused on two-finger manipulation. However, leveraging language to guide reinforcement learning for dexterous hands remains a challenge due to their high degrees of freedom. In this work, we introduce a language-guided dexterous multi-task manipulation framework (LDexMM), which decomposes the problem into two distinct phases. First, we use language instructions to guide a segmentation model in generating a dexterous grasp pose for the functional part of the object. After establishing this initial grasp, reinforcement learning is employed to refine the grasp pose and complete the task. Simultaneously, language constraints are applied to focus the actions on the specified object. Our experiments demonstrate success rates of 31%, 40%, 49.2%, and 72.7% on 10, 7, 5, and 3 tasks, respectively, with a single model.