Title: RoboMoRe: LLM-based Robot Co-design via Joint Optimization of Morphology and Reward Show Chinese title

Title: 基于LLM的机器人形态与奖励联合优化的机器人协同设计：RoboMoRe

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. Show Chinese abstract

Abstract: 机器人协同设计（同时优化形态和控制策略）一直是机器人学界长期面临的挑战，在这一领域已经开发出许多有前景的机器人。然而，一个关键的限制在于，由于使用固定的奖励函数，它往往倾向于收敛到次优设计，而这些固定奖励函数未能探索适合不同形态的不同运动模式。 在这里，我们提出了 RoboMoRe，这是一种由大型语言模型（LLM）驱动的框架，集成了形态和奖励塑造，用于机器人协同设计循环中的协同优化。RoboMoRe 执行双重阶段优化：在粗略优化阶段，基于 LLM 的多样性反射机制生成多样化且高质量的形态-奖励对，并高效地探索它们的分布。在精细优化阶段，通过交替的 LLM 引导的奖励和形态梯度更新，逐步完善候选者。 RoboMoRe 可以通过奖励塑造来优化高效的机器人形态及其合适的运动行为。结果显示，无需任何任务特定的提示或预定义的奖励/形态模板，RoboMoRe 在八个不同的任务中显著优于人为设计的方案和其他竞争方法。