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

Robot co-design, the joint optimization of morphology and control policy, remains a longstanding challenge in the robotics community. Existing approaches often converge to suboptimal designs because they rely on fixed reward functions, which fail to capture the diverse motion modes suited to different morphologies. We propose RoboMoRe, a large language model (LLM)-driven framework that integrates morphology and reward shaping for co-optimization within the robot design loop. RoboMoRe adopts a dual-stage strategy: in the coarse stage, an LLM-based Diversity Reflection mechanism is proposed to generate diverse and high-quality morphology–reward pairs and Morphology Screening is performed to reduce unpotential candidates and efficiently explore the design space; in the fine stage, top candidates are iteratively refined through alternating LLM-guided updates to both reward and morphology. This process enables RoboMoRe to discover efficient morphologies and their corresponding motion behaviors through joint optimization. The result across eight representative tasks demonstrate that without any task-specific prompting or predefined reward and morphology templates, RoboMoRe significantly outperform human-engineered design results and competing methods. Additional experiments demonstrate robustness of RoboMoRe on manipulation and free-form design tasks.