A Versatile Co-Design Approach For Dynamic Legged Robots
This addresses the challenge of efficient robot design for dynamic tasks in robotics, though it appears incremental as it builds on existing optimization methods with a novel derivative-based approach.
The paper tackles the problem of computationally co-designing legged robots and dynamic maneuvers by proposing a bilevel optimization approach that uses motion-planning derivatives, enabling optimization of morphology, payload, and actuator parameters under full dynamics and constraints. It demonstrates the method by designing quadruped robots for jumping and trotting, showing improved energy efficiency for a Solo robot.
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.