Reduced-order models (ROM) are popular in online motion planning due to their simplicity. A good ROM captures the bulk of the full model's dynamics while remaining low dimension. However, planning within the reduced-order space unavoidably constrains the full model, and hence we sacrifice the full potential of the robot. In the community of legged locomotion, this has lead to a search for better model extensions, but many of these extensions require human intuition, and there has not existed a principled way of evaluating the model performance and discovering new models. In this work, we propose a model optimization algorithm that automatically synthesizes reduced-order models, optimal with respect to any user-specified cost function. To demonstrate our work, we optimized models for a bipedal robot Cassie. We show in hardware experiment that the optimal ROM is simple enough for real time planning application and that the real robot achieves higher performance by using the optimal ROM.
翻译:减序模型( ROM) 在在线运动规划中很受欢迎, 因为它们简单。 一个好的 ROM 捕捉了整个模型的动态, 同时又保持低维度。 但是, 在减序空间内进行规划不可避免地会限制整个模型, 因此我们牺牲了机器人的全部潜力。 在腿部运动群中, 这导致寻找更好的模型扩展, 但许多这些扩展需要人类直觉, 并且没有评估模型性能和发现新模型的有原则的方法。 在这项工作中, 我们提出一个模型优化算法, 自动合成减序模型, 相对于任何用户指定的成本功能来说是最佳的。 为了展示我们的工作, 我们优化了双型机器人卡西的模型。 我们在硬件实验中显示, 最佳的ROM在实时规划应用上足够简单, 真正的机器人通过使用最佳的ROM实现更高的性能。