让机器人身体和大脑一起进化，摔倒后能更快恢复。
Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery
- 身体与控制策略同步优化，迭代提升抗摔能力。
- 在7个机器人上平均性能提升44.55%，身体优化贡献超40%。
- 适合研究人形机器人自主恢复与形态设计的学者。
人形机器人是具身智能的核心前沿，其类人形态使其可自然部署于人类工作空间。脑-体协同设计通过联合优化控制策略与物理形态，为实现这一潜力提供了新路径。跌倒恢复在此背景下成为关键能力，不仅提升安全性和韧性，还能自然融入运动系统，增强机器人自主性。本文提出 RoboCraft，一个可扩展的人形机器人协同设计框架，用于跌倒恢复优化。该框架通过控制策略与形态的耦合更新持续提升性能：共享策略在多个设计上预训练，再针对高性能形态逐步微调，实现高效适应而无需从头训练；同时，形态搜索由人体启发先验和优化算法驱动，并借助优先级缓冲区平衡对优秀候选者的重评估与新设计的探索。实验表明，RoboCraft 在7个公开人形机器人上平均性能提升44.55%，其中4个人形机器人的性能提升中至少40%由形态优化驱动，凸显了协同设计的关键作用。
原文摘要 · Abstract (English)
Humanoid robots represent a central frontier in embodied intelligence, as their anthropomorphic form enables natural deployment in humans' workspace. Brain-body co-design for humanoids presents a promising approach to realizing this potential by jointly optimizing control policies and physical morphology. Within this context, fall recovery emerges as a critical capability. It not only enhances safety and resilience but also integrates naturally with locomotion systems, thereby advancing the autonomy of humanoids. In this paper, we propose RoboCraft, a scalable humanoid co-design framework for fall recovery that iteratively improves performance through the coupled updates of control policy and morphology. A shared policy pretrained across multiple designs is progressively finetuned on high-performing morphologies, enabling efficient adaptation without retraining from scratch. Concurrently, morphology search is guided by human-inspired priors and optimization algorithms, supported by a priority buffer that balances reevaluation of promising candidates with the exploration of novel designs. Experiments show that RoboCraft achieves an average performance gain of 44.55% on seven public humanoid robots, with morphology optimization drives at least 40% of improvements in co-designing four humanoid robots, underscoring the critical role of humanoid co-design.
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