优化机器人设计与控制时，适当降低训练强度反而能提升最终性能。
An Empirical Study on the Computation Budget of Co-Optimization of Robot Design and Control in Simulation
- 在联合优化中控制训练强度适度降低，可提升后续精调效果。
- 控制训练预算越低，生成的设计越简单，复杂度受资源制约。
- 适用于机器人设计与控制协同优化的仿真研究，适合算法设计者参考。
机器人的设计（形状）通常在控制实现前确定，这可能限制其任务适应性，因为设计优劣取决于其任务表现，需设计与控制协同优化。本文通过四个不同仿真问题，实验研究了机器人设计与控制联合优化中的关键挑战。结果表明，在联合优化过程中降低控制器训练程度，反而能显著提升后续阶段对最优设计进行额外资源重训练后的性能。此外，分配给每个设计的控制器训练计算预算影响设计复杂度：预算越低，设计越简单。该研究验证了文献中关于联合优化的核心问题，揭示了计算资源配置对设计复杂度和最终性能的影响。
原文摘要 · Abstract (English)
The design (shape) of a robot is usually decided before the control is implemented. This might limit how well the design is adapted to a task, as the suitability of the design is given by how well the robot performs in the task, which requires both a design and a controller. The co-optimization or simultaneous optimization of the design and control of robots addresses this limitation by producing a design and control that are both adapted to the task. This paper investigates some of the challenges inherent in the co-optimization of design and control in simulation. The results show that reducing how well the controllers are trained during the co-optimization process significantly improves the robot's performance when considering a second phase in which the controller for the best design is retrained with additional resources. In addition, the results demonstrate that the computation budget allocated to training the controller for each design influences design complexity, with simpler designs associated with lower training budgets. This paper experimentally studies key questions discussed in other works in the literature on the co-optimization of design and control of robots in simulation in four different co-optimization problems.
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