# Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped

arXiv: https://arxiv.org/abs/1905.04254 · HTML: https://mlsanity.com/paper/1905.04254/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2019.

## Built on by (most cited first, 5 of 5)

- [HomeRobot: Open-Vocabulary Mobile Manipulation](https://mlsanity.com/paper/2306.11565/) (2023)
- [Automated Gait Generation For Walking, Soft Robotic Quadrupeds](https://mlsanity.com/paper/2310.00498/) (2023)
- [Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning](https://mlsanity.com/paper/2407.01050/) (2024)
- [Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learning](https://mlsanity.com/paper/2505.06561/) (2025)
- [asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics](https://mlsanity.com/paper/2604.24916/) (2026)

## Abstract (arXiv)

This paper presents Stanford Doggo, a quasi-direct-drive quadruped capable of dynamic locomotion. This robot matches or exceeds common performance metrics of state-of-the-art legged robots. In terms of vertical jumping agility, a measure of average vertical speed, Stanford Doggo matches the best performing animal and surpasses the previous best robot by 22%. An overall design architecture is presented with focus on our quasi-direct-drive design methodology. The hardware and software to replicate this robot is open-source, requires only hand tools for manufacturing and assembly, and costs less than 3000.