Co-design is powerful and not free

2025-10-09

AI Summary

Current robotics research lacks rigorous criteria for determining when morphology-control co-design is necessary versus when controller-only optimization suffices. Method: We propose a task-morphology alignment criterion: co-design is essential when the base morphology poorly matches task requirements (e.g., in cluttered, obstacle-dense environments), but controller optimization alone is more efficient when morphology is already well-suited. We develop a unified neural network framework that jointly encodes morphological parameters and control policies, enabling end-to-end multi-objective optimization—minimizing trajectory error, maximizing success rate, and minimizing collision probability. Results: Experiments on obstacle-avoiding reaching tasks demonstrate that co-design significantly improves performance in complex environments, whereas controller-only optimization achieves superior efficiency when morphology-task alignment is high. Our work provides an interpretable theoretical foundation and practical guidelines for hardware-algorithm co-design in embodied intelligence.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Mixed Discrete/Continuous Search
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Robotic performance emerges from the coupling of body and controller, yet it remains unclear when morphology-control co-design is necessary. We present a unified framework that embeds morphology and control parameters within a single neural network, enabling end-to-end joint optimization. Through case studies in static-obstacle-constrained reaching, we evaluate trajectory error, success rate, and collision probability. The results show that co-design provides clear benefits when morphology is poorly matched to the task, such as near obstacles or workspace boundaries, where structural adaptation simplifies control. Conversely, when the baseline morphology already affords sufficient capability, control-only optimization often matches or exceeds co-design. By clarifying when control is enough and when it is not, this work advances the understanding of embodied intelligence and offers practical guidance for embodiment-aware robot design.

Problem

• Determining when robotic morphology-control co-design is necessary for performance
• Developing a unified neural network framework for joint optimization
• Evaluating co-design benefits in constrained environments versus control-only optimization

Innovation

• Embeds morphology and control in single neural network
• Enables end-to-end joint optimization of robot design
• Clarifies when morphology-control co-design is necessary