A Versatile Co-Design Approach For Dynamic Legged Robots

TL;DR

This paper introduces a flexible bilevel optimization framework for the co-design of legged robots, enabling the simultaneous optimization of morphology, payload, and actuators considering full dynamics and constraints.

Contribution

It presents a novel derivative-based bilevel optimization method that integrates arbitrary differentiable motion planners for robot co-design.

Findings

Designed a more energy-efficient quadruped robot for jumping and trotting.

Demonstrated the framework's ability to incorporate complex physical constraints.

Optimized robot parameters considering full dynamics and physical limits.

Abstract

We present a versatile framework for the computational co-design of legged robots and dynamic maneuvers. Current state-of-the-art approaches are typically based on random sampling or concurrent optimization. We propose a novel bilevel optimization approach that exploits the derivatives of the motion planning sub-problem (i.e., the lower level). These motion-planning derivatives allow us to incorporate arbitrary design constraints and costs in an general-purpose nonlinear program (i.e., the upper level). Our approach allows for the use of any differentiable motion planner in the lower level and also allows for an upper level that captures arbitrary design constraints and costs. It efficiently optimizes the robot's morphology, payload distribution and actuator parameters while considering its full dynamics, joint limits and physical constraints such as friction cones. We demonstrate these capabilities by designing quadruped robots that jump and trot. We show that our method is able to design a more energy-efficient Solo robot for these tasks.