通过联合优化机器人形态与控制，揭示何时需协同设计。
Co-design is powerful and not free
- 将形态与控制参数统一建模于神经网络中，实现端到端联合优化。
- 在靠近障碍物或工作区边界时，协同设计可降低轨迹误差并减少碰撞概率。
- 若基础形态已适配任务，仅优化控制即可媲美甚至超越协同设计。
机器人性能源于身体与控制器的耦合，但形态与控制协同设计何时必要仍不明确。本文提出统一框架，将形态与控制参数嵌入单一神经网络，实现端到端联合优化。通过静态障碍物约束下的抓取任务案例研究，评估轨迹误差、成功率和碰撞概率。结果表明：当形态与任务不匹配（如靠近障碍物或工作区边界）时，结构适应性可简化控制，协同设计优势显著；而当基线形态已具备足够能力时，仅优化控制往往能匹配或超越协同设计。该研究厘清了控制优化的边界，深化了对具身智能的理解，并为感知具身设计提供实践指导。
原文摘要 · Abstract (English)
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.
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