*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Josie Giles
IRIM Marketing Communications Manager
josie@gatech.edu
Summary Sentence: Improving Multi-fingered Robot Manipulation by Unifying Learning and Planning
Full Summary: The Institute for Robotics and Intelligent Machines presents “Improving Multi-fingered Robot Manipulation by Unifying Learning and Planning” by Tucker Hermans of the University of Utah. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
Tucker HermansThe Institute for Robotics and Intelligent Machines presents “Improving Multi-fingered Robot Manipulation by Unifying Learning and Planning” by Tucker Hermans of the University of Utah. The event will be held in the Marcus Nanotechnology Building, Rooms 1116-1118, from 12:15-1:15 p.m. and is open to the public.
Abstract
Multi-fingered hands offer autonomous robots increased dexterity, versatility, and stability over simple two-fingered grippers. Naturally, this increased ability comes with increased complexity in planning and executing manipulation actions. As such, I propose combining model-based planning with learned components to improve over purely data-driven or purely-model based approaches to manipulation.
This talk examines multi-fingered autonomous manipulation when the robot has only partial knowledge of the object of interest. I will first present results on planning multi-fingered grasps for novel objects using a learned neural network. I will then present our approach to planning in-hand manipulation tasks when dynamic properties of objects are not known. I will conclude with a discussion of our ongoing and future research to further unify these two approaches.
Bio
Tucker Hermans is an assistant professor in the School of Computing at the University of Utah, where he is a founding member of the University of Utah Robotics Center. He was a visiting professor at NVIDIA Research during summer 2019. Hermans is a recipient of the NSF CAREER award and the 3M Non-Tenured Faculty Award. His research has been nominated for multiple conference paper awards, including winning the Best Medical Robotics Paper Award at ICRA 2017.
Previously, Hermans was a postdoctoral fellow at TU Darmstadt working with Jan Peters. He attended Georgia Tech from 2009 to 2014, in the School of Interactive Computing where he earned his Ph.D. in Robotics under the supervision of Aaron Bobick and Jim Rehg. At Georgia Tech he earned a M.Sc. in Computer Science. Additionally, Hermans received an A.B. in German and Computer Science from Bowdoin College in 2009.
