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.
Abstract (translated)
机器人性能源自机体与控制器的耦合，但形态和控制协同设计何时必要仍不清楚。我们提出了一种统一框架，在该框架中将形态参数和控制参数嵌入单个神经网络内，从而实现端到端联合优化。通过在静态障碍物约束下的抓取案例研究，我们评估了轨迹误差、成功率和碰撞概率。结果表明，在形态与任务不匹配的情况下（例如接近障碍物或工作空间边界时），协同设计提供了明显的好处，结构适应简化了控制。相反，当基线形态已经足够提供所需能力时，仅优化控制通常能与协同设计相匹敌甚至超越。 这项工作明确了何时控制就足够，何时又不够，并且通过阐明这种关系，它加深了对具身智能的理解，并为以身体意识为导向的机器人设计提供了实用指导。
URL
https://arxiv.org/abs/2510.08368
