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.

翻译：人形机器人是具身智能领域的核心前沿，其拟人形态使其能够自然地部署于人类工作空间。人形机器人的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有前景的途径。在此背景下，跌倒恢复能力成为一项关键功能。它不仅提升了安全性与鲁棒性，还能自然地与运动系统集成，从而推动人形机器人的自主性发展。本文提出RoboCraft，一个面向跌倒恢复的可扩展人形机器人协同设计框架，通过控制策略与形态的耦合更新迭代提升性能。一个在多种设计上预训练的共享策略，在高性能形态上逐步进行微调，实现了无需从头重新训练的高效适应。同时，形态搜索受到人类启发的先验知识与优化算法的引导，并辅以一个优先级缓冲区，该缓冲区平衡了对有潜力候选设计的重新评估与对新设计的探索。实验表明，RoboCraft在七个公开人形机器人上平均实现了44.55%的性能提升，在协同设计四个人形机器人时，形态优化贡献了至少40%的改进，突显了人形机器人协同设计的关键作用。