Optimal Reduced-order Modeling of Bipedal Locomotion
This work addresses the problem of enabling more capable bipedal locomotion for robotics by incrementally improving reduced-order modeling.
The paper tackles the limitation of existing reduced-order models like LIP and SLIP in executing complex or agile bipedal locomotion tasks by automatically synthesizing low-dimensional models that retain high-dimensional system capabilities, resulting in optimized models for walking at various speeds and inclines on a five-link model and the Cassie robot.
State-of-the-art approaches to legged locomotion are widely dependent on the use of models like the linear inverted pendulum (LIP) and the spring-loaded inverted pendulum (SLIP), popular because their simplicity enables a wide array of tools for planning, control, and analysis. However, they inevitably limit the ability to execute complex tasks or agile maneuvers. In this work, we aim to automatically synthesize models that remain low-dimensional but retain the capabilities of the high-dimensional system. For example, if one were to restore a small degree of complexity to LIP, SLIP, or a similar model, our approach discovers the form of that additional complexity which optimizes performance. In this paper, we define a class of reduced-order models and provide an algorithm for optimization within this class. To demonstrate our method, we optimize models for walking at a range of speeds and ground inclines, for both a five-link model and the Cassie bipedal robot.