Beyond Inverted Pendulums: Task-optimal Simple Models of Legged Locomotion

TL;DR

This paper introduces a novel algorithm for automatically synthesizing reduced-order models tailored to specific tasks, significantly improving control efficiency and performance in legged robot locomotion, demonstrated on the Cassie robot.

Contribution

The authors develop a model optimization method that automatically creates task-specific reduced-order models, removing the need for human intuition and enabling better control performance.

Findings

Optimized models reduce Cassie's joint torque costs by up to 23%.

Optimized models increase Cassie's walking speed by up to 54%.

Real robot experiments show 10% lower torque cost during walking.

Abstract

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.