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

Guiliang Liu, Bo Yue, Yi Jin Kim, Kui Jia

TL;DR

This paper argues that true embodied intelligence in humanoid robots requires co-design of both control policies and physical morphology, framing the problem as a bi-level optimization that integrates learning with design within an embodied reasoning–acting architecture. It outlines a feasible path via strategic exploration, Sim2Real transfer, and meta-policy learning to jointly optimize morphology and control, rather than relying on fixed structures. The authors emphasize the methodological, application-driven, and community benefits of co-design, proposing principled optimization of morphology, adaptive body shaping for real-world tasks, and cross-disciplinary collaboration as key pillars. They also discuss open questions and long-term goals, including world-model–assisted co-design and design-aware policies, highlighting the potential to produce more robust, general-purpose humanoid agents. Overall, the work positions body-control co-design as a core strategy for developing intelligent, adaptable humanoid robots that can operate effectively across diverse environments and tasks.

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.

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

TL;DR

This paper argues that true embodied intelligence in humanoid robots requires co-design of both control policies and physical morphology, framing the problem as a bi-level optimization that integrates learning with design within an embodied reasoning–acting architecture. It outlines a feasible path via strategic exploration, Sim2Real transfer, and meta-policy learning to jointly optimize morphology and control, rather than relying on fixed structures. The authors emphasize the methodological, application-driven, and community benefits of co-design, proposing principled optimization of morphology, adaptive body shaping for real-world tasks, and cross-disciplinary collaboration as key pillars. They also discuss open questions and long-term goals, including world-model–assisted co-design and design-aware policies, highlighting the potential to produce more robust, general-purpose humanoid agents. Overall, the work positions body-control co-design as a core strategy for developing intelligent, adaptable humanoid robots that can operate effectively across diverse environments and tasks.

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.

Paper Structure

This paper contains 19 sections, 3 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Embodied Humanoid Robots
  3. Architecture of Humanoid Robot
  4. Embodied Intelligence in Humanoid Robot
  5. Implementation for Embodied Humanoid Robot.
  6. Position Proposal and Alternative Views
  7. A New Perspective: Co-Designing Control and Evolution Policies
  8. Alternative Views: Intelligence Arises from Fixed Humanoid Robots Structure
  9. Feasibility of Co-Designing Humanoid Robot
  10. Strategic Exploration under Constrained Design Space
  11. A Sim2Real Paradigm for Evaluation and Deployment
  12. Controlling Humanoid Robot via Meta Policy
  13. Necessity of Co-Designing Humanoid Robot
  14. Methodology: Principled Optimization of Robot Morphology
  15. Application: Adaptive Body Shaping for Real-World Tasks
  16. ...and 4 more sections

Figures (2)

  • Figure 1: The co-design framework for humanoid robots, which can be formulated as a bi-level optimization problem, consisting of two interconnected phases: 1) learning the control policy for the humanoid robot, and 2) designing the robot's physical structure (Section \ref{['subsec:co-design-formulation']}).
  • Figure 2: Examples of humanoid robots with varying physical structures are shown from left to right: full-sized, simplified, wheeled, and child-sized humanoid robots.