Max Robotics — Global Service Robot Directory + US Certification

About

Jethro Tan is a leading roboticist whose work sits at the intersection of natural language processing, computer vision, and robotic manipulation. His research focuses on enabling robots to understand and interact with the physical world more intuitively, bridging the gap between human communication and machine action. Tan’s most impactful contribution is his pioneering work on spoken language comprehension for robots, as demonstrated in his highly cited 2018 paper “Interactively Picking Real-World Objects with Unconstrained Spoken Language Instructions” (175 citations), which tackles the challenge of parsing complex, ambiguous human speech to guide object manipulation. He also made a landmark achievement 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 pick-and-place operations in unstructured warehouse environments. Further expanding robotic perception, Tan developed deep visuo-tactile learning methods that estimate tactile properties like slipperiness from images alone (70 citations), enabling safer and more adaptive interactions. His end-to-end learning approach for object grasp poses (20 citations) and multi-policy reinforcement learning for adaptability (9 citations) round out a career dedicated to creating more capable, communicative, and resilient robots.