{"ID":2867115,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.18937","arxiv_id":"2509.18937","title":"Lang2Morph: Language-Driven Morphological Design of Robotic Hands","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.","short_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...","url_abs":"https://arxiv.org/abs/2509.18937","url_pdf":"https://arxiv.org/pdf/2509.18937v1","authors":"[\"Yanyuan Qiao\",\"Kieran Gilday\",\"Yutong Xie\",\"Josie Hughes\"]","published":"2025-09-23T12:54:52Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}