Papers
9
Total Citations
383
H-Index
7
About
Wilson Ko is a leading robotics researcher whose work bridges the gap between human intention and robotic action. His primary research areas include human-robot interaction, natural language processing for robotics, and autonomous manipulation in unstructured environments. Ko’s most impactful contribution is his pioneering work on enabling robots to understand and act upon unconstrained spoken language instructions, as demonstrated in his highly cited 2018 paper (175 citations) on interactively picking real-world objects. He achieved international recognition as a key member of Team Delft, which won both the Picking and Stowing competitions at the Amazon Picking Challenge 2016 (127 citations), showcasing robust solutions for warehouse automation. Ko has also advanced the field of food robotics with his uncertainty-aware, self-supervised approach to grasping granular foods (26 citations), addressing a critical need in the food packing industry. His work on learning from demonstration (22 citations) further highlights his commitment to making industrial robots easily reprogrammable for high-mix production. With a total of over 380 citations, Ko’s research continues to shape the future of intelligent, adaptable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Team Delft’s Robot Winner of the Amazon Picking Challenge 2016127 citations · 2017
- 3Uncertainty-aware Self-supervised Target-mass Grasping of Granular Foods26 citations · 2021
- 4Towards Industrial Robot Learning from Demonstration22 citations · 2015
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- 6
- 7Team Delft's Robot Winner of the Amazon Picking Challenge 20167 citations · 2016
- 8LAP2 citations · 2016
- 9Uncertainty-Aware Self-Supervised Target-Mass Grasping of Granular Foods2 citations · 2021