超越倒立摆：腿足运动的任务最优简单模型
Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion
AI总结：
本文提出一种面向用户指定任务分布与代价函数的降阶模型自动优化算法，并在双足机器人 Cassie 上验证，显著降低力矩代价并提升行走速度。
摘要：
降阶模型（ROM）因其简单性而广泛用于在线运动规划。一个适合控制的良好 ROM 既能捕捉完整动力学中与任务相关的关键方面，又能保持低维特性。然而，在降阶空间内进行规划不可避免地会约束完整模型，因此我们牺牲了机器人的全部潜力。在腿足运动领域，这促使人们寻找更好的模型扩展，但许多此类扩展都依赖人类直觉，而且此前尚不存在一种有原则的方法来评估模型性能并发现新模型。在本工作中，我们提出一种模型优化算法，可自动综合降阶模型；这些模型相对于用户指定的任务分布及相应代价函数是最优的。为展示我们的工作，我们为双足机器人 Cassie 优化了模型。仿真结果表明，最优 ROM 将 Cassie 的关节力矩代价最高降低 23%，并将其行走速度最高提高 54%。我们还展示了硬件结果：真实机器人在平地上行走时，力矩代价降低 10%。所有视频和代码可在 https://sites.google.com/view/ymchen/research/optimal-rom 获取。
英文摘要：
Reduced-order models (ROM) are popular in online motion planning due to their simplicity. A good ROM for control captures critical task-relevant aspects of the full dynamics while remaining low dimensional. 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 a user-specified distribution of tasks and corresponding cost functions. To demonstrate our work, we optimized models for a bipedal robot Cassie. We show in simulation that the optimal ROM reduces the cost of Cassie's joint torques by up to 23% and increases its walking speed by up to 54%. We also show hardware result that the real robot walks on flat ground with 10% lower torque cost. All videos and code can be found at https://sites.google.com/view/ymchen/research/optimal-rom.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
