Mechanical Intelligence in Robotic Manipulation: Towards Human-Level Dexterity in Robotic and Prosthetic Hands

Dollar, Aaron M.

Title:

Mechanical Intelligence in Robotic Manipulation: Towards Human-Level Dexterity in Robotic and Prosthetic Hands

dc.contributor.author	Dollar, Aaron M.
dc.contributor.corporatename	Georgia Institute of Technology. Institute for Robotics and Intelligent Machines	en_US
dc.contributor.corporatename	Yale University	en_US
dc.date.accessioned	2018-10-26T20:32:25Z
dc.date.available	2018-10-26T20:32:25Z
dc.date.issued	2018-10-10
dc.description	Presented on October 10, 2018 from 12:15 p.m.-1:15 p.m. in the Marcus Nanotechnology Building, Rooms 1116-1118, Georgia Tech.	en_US
dc.description	Aaron M. Dollar is an associate professor of Mechanical Engineering and Materials Science at Yale University. He earned a B.S. in Mechanical Engineering at the University of Massachusetts at Amherst, S.M. and Ph.D. degrees in Engineering Science at Harvard University, and was a postdoctoral associate at MIT in Health Sciences and Technology and the Media Lab. He is the recipient of a number of awards, including young investigator awards from AFOSR, DARPA, NASA, and NSF, and is the founder of the IEEE Robotics and Automation Society Technical Committee on Mechanisms and Design. Dollar’s research interests include mechatronics, robotic grasping and manipulation, machine and mechanism design, rehabilitation and assistive devices, prosthetics, underactuated mechanisms, and biomechanics of human movement.	en_US
dc.description	Runtime: 64:04 minutes	en_US
dc.description.abstract	The human hand is the pinnacle of dexterity – it has the ability to powerfully grasp a wide range of object sizes and shapes as well as delicately manipulate objects held within the fingertips. Current robotic and prosthetic systems, however, have only a fraction of that manual dexterity. My group attempts to address this gap in three main ways: examining the mechanics and design of effective hands, studying biological hand function as inspiration and performance benchmarking, and developing novel control approaches that accommodate task uncertainty. In terms of hand design, we strongly prioritize passive mechanics, including incorporating adaptive underactuated transmissions and carefully tuned compliance, and seek to maximize open-loop performance while minimizing complexity. To motivate and benchmark our efforts, we are examining human hand usage during daily activities as well as quantifying functional aspects such as precision manipulation workspaces.	en_US
dc.format.extent	64:04 minutes
dc.identifier.uri	http://hdl.handle.net/1853/60500
dc.language.iso	en_US	en_US
dc.publisher	Georgia Institute of Technology	en_US
dc.relation.ispartofseries	IRIM Seminar Series
dc.subject	Grasping and manipulation	en_US
dc.subject	Mechanical design	en_US
dc.subject	Robotics	en_US
dc.title	Mechanical Intelligence in Robotic Manipulation: Towards Human-Level Dexterity in Robotic and Prosthetic Hands	en_US
dc.type	Moving Image
dc.type.genre	Lecture
dspace.entity.type	Publication
local.contributor.corporatename	Institute for Robotics and Intelligent Machines (IRIM)
local.relation.ispartofseries	IRIM Seminar Series
relation.isOrgUnitOfPublication	66259949-abfd-45c2-9dcc-5a6f2c013bcf
relation.isSeriesOfPublication	9bcc24f0-cb07-4df8-9acb-94b7b80c1e46