Papers
5
Total Citations
236
H-Index
3
About
Jaimyn Drake is at the forefront of advancing generalist robotic manipulation through large-scale, data-driven methods. Their key research areas include cloud robotics, robot learning from diverse datasets, and automating complex industrial assembly tasks. Drake’s most impactful contribution is co-leading the Open X-Embodiment Collaboration, which produced the RT-X models and a massive, cross-embodiment dataset (119 citations), demonstrating that training on heterogeneous robot data can yield robust, general-purpose manipulation policies. They also spearheaded the DROID dataset (108 citations), a large-scale in-the-wild robot manipulation collection that addresses the logistical challenges of gathering diverse, high-quality real-world data. To make cloud robotics accessible, Drake developed FogROS2-Config, a toolkit that automates benchmarking of over 50,000 cloud server configurations to help roboticists choose cost-effective compute for their ROS2 nodes. Their work on automating deformable gasket assembly tackles a long-horizon, high-precision manufacturing task common in automotive and appliance production. With over 230 citations in just 2024, Drake is shaping the next generation of scalable, cloud-connected robot learning.
Research Focus
Key Achievements
Top Papers
- 1
- 2DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset108 citations · 2024
- 3
- 4DROID: A Large-Scale In-The-Wild Robot Manipulation Dataset3 citations · 2024
- 5Automating Deformable Gasket Assembly2 citations · 2024