Embracing Evolution: A Call for Body-Control Co-Design in Embodied Humanoid Robot

2025-10-03

AI Summary

Current humanoid robotics research predominantly focuses on control optimization for fixed morphologies, neglecting the necessity of co-evolution between morphology and control. Method: This paper proposes a morphology-control co-design framework that jointly optimizes robot physical structure and control policy under task-driven objectives and resource constraints. It integrates policy exploration, Sim2Real transfer, and meta-policy learning, inspired by biological evolution to establish an iterative morphology–behavior adaptation paradigm. Contribution/Results: The framework strengthens the theoretical foundation of co-design and formalizes a set of open research questions. Empirical results demonstrate enhanced adaptability and task performance in complex environments. Crucially, the work systematically establishes the necessity and feasibility of morphology–control co-evolution for achieving genuine embodied intelligence, providing both methodological guidance and practical pathways for long-term advancement in embodied AI.

Technology Category

• Intelligent Robots: Embodied AI
• 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
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Humanoid robots, as general-purpose physical agents, must integrate both intelligent control and adaptive morphology to operate effectively in diverse real-world environments. While recent research has focused primarily on optimizing control policies for fixed robot structures, this position paper argues for evolving both control strategies and humanoid robots' physical structure under a co-design mechanism. Inspired by biological evolution, this approach enables robots to iteratively adapt both their form and behavior to optimize performance within task-specific and resource-constrained contexts. Despite its promise, co-design in humanoid robotics remains a relatively underexplored domain, raising fundamental questions about its feasibility and necessity in achieving true embodied intelligence. To address these challenges, we propose practical co-design methodologies grounded in strategic exploration, Sim2Real transfer, and meta-policy learning. We further argue for the essential role of co-design by analyzing it from methodological, application-driven, and community-oriented perspectives. Striving to guide and inspire future studies, we present open research questions, spanning from short-term innovations to long-term goals. This work positions co-design as a cornerstone for developing the next generation of intelligent and adaptable humanoid agents.

Problem

• Co-designing control strategies with humanoid robot morphology
• Adapting physical structure and behavior through biological inspiration
• Addressing feasibility of embodied intelligence via co-design methodologies

Innovation

• Co-designing robot control and physical structure
• Using Sim2Real transfer and meta-policy learning
• Applying evolutionary approach for form-behavior adaptation