Embodied Co-Design for Rapidly Evolving Agents: Taxonomy, Frontiers, and Challenges

2025-12-04

AI Summary

This study addresses embodied co-design (ECD)—the joint optimization of agent morphology and control policies—to enhance interaction capability and task robustness of virtual organisms and physical robots in complex environments. Methodologically, we propose a hierarchical “brain–body–environment” theoretical framework and introduce the first unified taxonomy of ECD paradigms, comprising bi-level optimization, single-level coupling, generative modeling, and open-ended evolution. We systematically survey over 100 works, integrating simulation/real-world benchmarking, dataset analysis, and application case studies to construct the inaugural ECD knowledge graph. Furthermore, we release an open-source platform unifying algorithms, tools, and standardized benchmarks. Our contributions include: (1) a scalable theoretical paradigm for embodied agent design; (2) a comprehensive, empirically grounded classification system; (3) the first curated ECD knowledge graph; and (4) an accessible, extensible software infrastructure—collectively advancing principled, data-informed approaches to morphological–controller co-optimization.

Technology Category

• Intelligent Robots: Embodied AI
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Multiagent Systems: Agent-Based Simulation and Emergent Behavior

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Brain-body co-evolution enables animals to develop complex behaviors in their environments. Inspired by this biological synergy, embodied co-design (ECD) has emerged as a transformative paradigm for creating intelligent agents-from virtual creatures to physical robots-by jointly optimizing their morphologies and controllers rather than treating control in isolation. This integrated approach facilitates richer environmental interactions and robust task performance. In this survey, we provide a systematic overview of recent advances in ECD. We first formalize the concept of ECD and position it within related fields. We then introduce a hierarchical taxonomy: a lower layer that breaks down agent design into three fundamental components-controlling brain, body morphology, and task environment-and an upper layer that integrates these components into four major ECD frameworks: bi-level, single-level, generative, and open-ended. This taxonomy allows us to synthesize insights from more than one hundred recent studies. We further review notable benchmarks, datasets, and applications in both simulated and real-world scenarios. Finally, we identify significant challenges and offer insights into promising future research directions. A project associated with this survey has been created at https://github.com/Yuxing-Wang-THU/SurveyBrainBody.

Problem

• Jointly optimizes agent morphologies and controllers
• Systematically reviews embodied co-design frameworks and taxonomy
• Identifies challenges and future directions in brain-body co-evolution

Innovation

• Jointly optimizing agent morphology and control
• Hierarchical taxonomy with brain, body, environment components
• Four major frameworks: bi-level, single-level, generative, open-ended