Learning to Grasp, with Jeannette Bohg
In this episode, Lilly Clark interviews Jeannette Bohg, Assistant Professor at Stanford, about her work in interactive perception and robot learning for grasping and manipulation tasks. Bohg discusses how robots and humans are different, the challenge of high dimensional data, and unsolved problems including continuous learning and decentralized manipulation.
Jeannette Bohg is an Assistant Professor of Computer Science at Stanford University. She was a group leader at MPI until September 2017 and remains affiliated as a guest researcher. Her research focuses on perception for autonomous robotic manipulation and grasping. She is specifically interested in developing methods that are goal-directed, real-time and multi-modal such that they can provide meaningful feedback for execution and learning.
Before joining the Autonomous Motion lab in January 2012, Jeannette Bohg was a PhD student at the Computer Vision and Active Perception lab (CVAP) at KTH in Stockholm. Her thesis on Multi-modal scene understanding for Robotic Grasping was performed under the supervision of Prof. Danica Kragic. She studied at Chalmers in Gothenburg and at the Technical University in Dresden where she received her Masters in Art and Technology and her Diploma in Computer Science, respectively.
Robohub Podcast is a non-profit robotics podcast where we interview experts in robotics, including researchers, entrepreneurs, policy makers, and venture capitalists. Our interviewers are researchers, entrepreneurs, and engineers involved in robotics. Our interviews are technical and, often, get into the details of what we are discussing, but we make an effort to have our interviews understandable to a general audience.