Stanford Doggo: An Open-Source, Quasi-Direct-Drive Quadruped
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 10 May 2019.
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Citing papers per year: 2023: 2; 2024: 1; 2025: 1; 2026: 1.
5 papers.
- 2023HomeRobot: Open-Vocabulary Mobile Manipulationcited by 104
- 2023Automated Gait Generation For Walking, Soft Robotic Quadrupedscited by 1
- 2024Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learningcited by 0
- 2025Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learningcited by 0
- 2026asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamicscited by 0
- 2026asRoBallet: Closing the Sim2Real Gap via Friction-Aware Reinforcement Learning for Underactuated Spherical Dynamics27 Apr 2026
- 2025Quadrupedal Robot Skateboard Mounting via Reverse Curriculum Learning10 May 2025
- 2024Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning1 Jul 2024
- 2023Automated Gait Generation For Walking, Soft Robotic Quadrupeds30 Sep 2023
- 2023HomeRobot: Open-Vocabulary Mobile Manipulation20 Jun 2023