# A Versatile Co-Design Approach For Dynamic Legged Robots

arXiv: https://arxiv.org/abs/2103.04660 · HTML: https://mlsanity.com/paper/2103.04660/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2021.

## Built on by (most cited first, 4 of 4)

- [Meta Reinforcement Learning for Optimal Design of Legged Robots](https://mlsanity.com/paper/2210.02750/) (2022)
- [House of Dextra: Cross-embodied Co-design for Dexterous Hands](https://mlsanity.com/paper/2512.03743/) (2025)
- [Structural Optimization of Lightweight Bipedal Robot via SERL](https://mlsanity.com/paper/2408.15632/) (2024)
- [GLAMDRING: Gait Learning And Morphology co-Design via Reinforcement LearnING of CPGs](https://mlsanity.com/paper/2609.19452/) (2026)

## Abstract (arXiv)

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.