Human hands are amazing. They can grasp and manipulate complex objects. They can apply pressure with strength or with subtlety. We'd like robots to be to perform manipulation like humans can, and we'd like robots to understand how humans are using their hands in shared environments. In this talk I'll present a line of research that measures the contact and pressure of human hands by using thermal imagery and pressure sensors, then tries to learn to predict these attributes from various modalities, and finally tries to transfer some of the findings to robots.

Bio:

James is an associate professor of computing at Georgia Institute of Technology. From 2017 to 2022 he was also a principal scientist at Argo AI. Previously, James was the Manning assistant professor of computer science at Brown University. James received his Ph.D. from Carnegie Mellon University and was a postdoc at Massachusetts Institute of Technology. His research interests span computer vision, robotics, and machine learning. His research often involves finding new data sources to exploit (e.g. geotagged imagery, thermal imagery) or creating new datasets where none existed (e.g. human sketches, HD maps). James is the recipient of the NSF CAREER award and Sloan Fellowship.