Toward Humanoid Brain-Body Co-design: Joint Optimization of Control and Morphology for Fall Recovery

TL;DR

This paper introduces RoboCraft, a scalable framework for joint optimization of control and morphology in humanoid robots to enhance fall recovery, demonstrating significant performance improvements across multiple robot designs.

Contribution

The paper presents RoboCraft, a novel co-design framework that iteratively optimizes control policies and morphology for humanoids, improving fall recovery capabilities.

Findings

Achieves an average of 44.55% performance gain on seven humanoid robots.

Morphology optimization accounts for at least 40% of co-design improvements.

Shared policy fine-tuning enables efficient adaptation across designs.

Abstract

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.