Language-Guided Dexterous Functional Grasping by LLM Generated Grasp Functionality and Synergy for Humanoid Manipulation

Dexterous Functional Grasping (DFG) is the crucial first step for humanoid robots to perform generalized manipulation tasks. However, enabling robots to learn language-guided DFG skills in real-world environments presents several challenges, including comprehending the complex relationship between task instructions and grasp functionality, generating feasible functional grasps of dexterous hands, and handling generalization for novel functional concepts. To tackle these challenges, we introduce SayFuncGrasp, a Large Language Model (LLM) based DFG framework that can synthesize versatile dexterous functional grasps from language instructions and achieve generalization on novel functional concepts. SayFuncGrasp first harnesses the open-ended manipulation knowledge from an LLM to infer grasp functionality based on language instructions. Subsequently, it employs the inferred grasp functionality to synthesize plausible DFG actions characterized by hand synergies. Simulation experiments show that SayFuncGrasp significantly outperforms the baseline method in open-set grasp functionality generalization. Real robot experiments demonstrate the effectiveness and generalizability of SayFuncGrasp for interactive humanoid manipulation tasks, achieving an overall grasp success rate of 64.66% and a manipulation success rate of 70.41%. Note to Practitioners—This research was motivated by the practical challenge of enabling humanoid robots with high-DoF dexterous hands to perform functional grasping based on verbal instructions. In industrial settings, such capabilities can significantly enhance the versatility and adaptability of humanoid assistants, allowing them to perform complex manipulations simply by being told what to do, thereby reducing programming complexity and increasing flexibility. Current dexterous functional grasping methods rely solely on visual input, without the ability to process language instructions. Furthermore, they are restricted to pre-defined functional concepts and cannot be generalized to novel object classes and manipulation tasks within natural language. Our newly proposed language-guided dexterous functional grasping system takes advantage of open-ended manipulation knowledge from LLMs to produce generalized functional grasps of dexterous robot hands according to verbal commands. Our experiment results demonstrate improved versatility and generalizability compared to the state-of-the-art.