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

2025-10-25

AI Summary

To address the insufficient fall recovery capability of humanoid robots, this work proposes a brain-body co-design paradigm that jointly optimizes control policies and physical morphology. Methodologically, we introduce RoboCraft: a framework that fine-tunes policies within an efficient morphological search space using a pre-trained shared policy; incorporates human motion priors to guide morphology optimization; and employs an iterative joint optimization algorithm integrating morphology-prioritized experience replay, reinforcement learning policy transfer, and multi-objective morphological evolution. Experiments across seven public humanoid platforms demonstrate an average 44.55% improvement in fall recovery success rate, with morphology optimization alone contributing ≥40% of the performance gain. To our knowledge, this is the first work achieving closed-loop co-evolution of control and morphology, significantly enhancing robotic safety and autonomy.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

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 ourmethod{} 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.

Problem

• Jointly optimizing control policies and humanoid robot morphology
• Developing scalable co-design framework for fall recovery capability
• Enhancing humanoid autonomy through brain-body coupled optimization

Innovation

• Joint optimization of control policy and morphology
• Shared policy pretrained and progressively finetuned
• Morphology search guided by human-inspired priors