Elliott Donlon
Papers
7
Total Citations
793
H-Index
7
About
Elliott Donlon is a leading roboticist whose work sits at the intersection of robotic manipulation, computer vision, and tactile sensing. He is best known for his pioneering contributions to the Amazon Picking Challenge, where he was a key member of Team MIT, developing fully automated solutions for warehouse picking—a foundational achievement in logistics robotics. His most influential work, a system for pick-and-place of novel objects in clutter, has garnered over 700 citations across its versions. This system introduced multi-affordance grasping and cross-domain image matching, enabling robots to handle a wide range of object categories without task-specific training data. Beyond grasping, Donlon has advanced tactile sensing with the development of GelSlim, a high-resolution, compact tactile-sensing finger that is more robust and slimmer than prior designs. His research on tactile regrasp and incipient slip detection has further pushed the boundaries of dexterous manipulation, allowing robots to adjust grasps and maintain hold on objects by predicting slip before it occurs. Donlon’s work is essential reading for anyone interested in building robots that can perceive and interact with the physical world as adeptly as humans.
Research Focus
Key Achievements
Top Papers
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- 4A Summary of Team MIT's Approach to the Amazon Picking Challenge 201543 citations · 2016
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- 6Tactile Regrasp: Grasp Adjustments via Simulated Tactile Transformations13 citations · 2018
- 7Maintaining Grasps within Slipping Bound by Monitoring Incipient Slip11 citations · 2018