RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866918041406668800 |
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| author | Fang, Jiawei Sun, Yuxuan Ma, Chengtian Lu, Qiuyu Yao, Lining |
| author_facet | Fang, Jiawei Sun, Yuxuan Ma, Chengtian Lu, Qiuyu Yao, Lining |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00276 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward Fang, Jiawei Sun, Yuxuan Ma, Chengtian Lu, Qiuyu Yao, Lining Robotics Computation and Language 68T40, 68T05, 90C90 I.2.9; I.2.6; I.2.8; I.2.10 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. |
| title | RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward |
| topic | Robotics Computation and Language 68T40, 68T05, 90C90 I.2.9; I.2.6; I.2.8; I.2.10 |
| url | https://arxiv.org/abs/2506.00276 |