📝 SummarXiv

Abstract

Designing robotic hand morphologies for diverse manipulation tasks requires balancing dexterity, manufacturability, and task-specific functionality. While open-source frameworks and parametric tools support reproducible design, they still rely on expert heuristics and manual tuning. Automated methods using optimization are often compute-intensive, simulation-dependent, and rarely target dexterous hands. Large language models (LLMs), with their broad knowledge of human-object interactions and strong generative capabilities, offer a promising alternative for zero-shot design reasoning. In this paper, we present Lang2Morph, a language-driven pipeline for robotic hand design. It uses LLMs to translate natural-language task descriptions into symbolic structures and OPH-compatible parameters, enabling 3D-printable task-specific morphologies. The pipeline consists of: (i) Morphology Design, which maps tasks into semantic tags, structural grammars, and OPH-compatible parameters; and (ii) Selection and Refinement, which evaluates design candidates based on semantic alignment and size compatibility, and optionally applies LLM-guided refinement when needed. We evaluate Lang2Morph across varied tasks, and results show that our approach can generate diverse, task-relevant morphologies. To our knowledge, this is the first attempt to develop an LLM-based framework for task-conditioned robotic hand design.