Robohub.org
ep.

309

podcast
 

Learning to Grasp with Jeannette Bohg

by
11 May 2020



share this:


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.

Links



tags: , ,


Lilly Clark





Related posts :



Countering Luddite politicians with life (and cost) saving machines

Beyond aerial tricks, drones are now being deployed in novel ways to fill the labor gap of menial jobs that have not returned since the pandemic.
04 December 2022, by

Call for robot holiday videos 2022

That’s right! You better not run, you better not hide, you better watch out for brand new robot holiday videos on Robohub!
02 December 2022, by

The Utah Bionic Leg: A motorized prosthetic for lower-limb amputees

Lenzi’s Utah Bionic Leg uses motors, processors, and advanced artificial intelligence that all work together to give amputees more power to walk, stand-up, sit-down, and ascend and descend stairs and ramps.

Touch sensing: An important tool for mobile robot navigation

Proximal sensing often is a blind spot for most long range sensors such as cameras and lidars for which touch sensors could serve as a complementary modality.
29 November 2022, by

Study: Automation drives income inequality

New data suggest most of the growth in the wage gap since 1980 comes from automation displacing less-educated workers.
27 November 2022, by

Flocks of assembler robots show potential for making larger structures

Researchers make progress toward groups of robots that could build almost anything, including buildings, vehicles, and even bigger robots.
25 November 2022, by





©2021 - ROBOTS Association


 












©2021 - ROBOTS Association