RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward

• URL: http://arxiv.org/abs/2506.00276v1
• Date: Fri, 30 May 2025 22:16:07 GMT
• Title: RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward
• Authors: Jiawei Fang, Yuxuan Sun, Chengtian Ma, Qiuyu Lu, Lining Yao,
• Abstract summary: RoboMoRe is a framework that integrates morphology and reward shaping for co-optimization within the robot co-design loop.<n>In the coarse optimization stage, an LLM-based diversity reflection mechanism generates both diverse and high-quality morphology-reward pairs.<n>In the fine optimization stage, top candidates are iteratively refined through alternating LLM-guided reward and morphology gradient updates.
• Score: 21.110738533383277
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robot co-design, jointly optimizing morphology and control policy, remains a longstanding challenge in the robotics community, where many promising robots have been developed. However, a key limitation lies in its tendency to converge to sub-optimal designs due to the use of fixed reward functions, which fail to explore the diverse motion modes suitable for different morphologies. Here we propose RoboMoRe, a large language model (LLM)-driven framework that integrates morphology and reward shaping for co-optimization within the robot co-design loop. RoboMoRe performs a dual-stage optimization: in the coarse optimization stage, an LLM-based diversity reflection mechanism generates both diverse and high-quality morphology-reward pairs and efficiently explores their distribution. In the fine optimization stage, top candidates are iteratively refined through alternating LLM-guided reward and morphology gradient updates. RoboMoRe can optimize both efficient robot morphologies and their suited motion behaviors through reward shaping. Results demonstrate that without any task-specific prompting or predefined reward/morphology templates, RoboMoRe significantly outperforms human-engineered designs and competing methods across eight different tasks.

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