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Incremental Learning for Robot Sensing and Control

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56HZV-10-C-0176
Agency Tracking Number: A09A-030-0258
Amount: $99,958.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: A09A-T030
Solicitation Number: 2009.A
Timeline
Solicitation Year: 2009
Award Year: 2010
Award Start Date (Proposal Award Date): 2010-01-21
Award End Date (Contract End Date): 2010-07-21
Small Business Information
1005 N. Glebe Rd. Suite 400
Arlington, VA 22201
United States
DUNS: 009425005
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 David Coombs
 Research Scientist
 (703) 738-6279
 dcoombs@setcorp.com
Business Contact
 Mary Beth House
Title: Contracts Manager
Phone: (703) 738-6217
Email: mhouse@setcorp.com
Research Institution
 Carnegie Mellon University
 Michael Happold
 
5000 Forbes Avenue
Pittsburgh, PA 15213-
United States

 (412) 681-8678
 Nonprofit college or university
Abstract

SET Corporation, together with Carnegie Mellon University''s National Robotics Engineering Center (NREC), will develop a system that leverages state-of-the-art sensing, perception, and machine learning to provide trafficability assessments for UGVs for agricultural, security and military applications. It will consist of a set of proprioceptive and exteroceptive sensors that provide rich data about the UGV’s environment in conjunction with a learning system that supports a combined experiential and imitative learning regime. We propose a 6 month Phase I effort to 1) develop the underlying algorithms for a combined incremental experiential and imitative learning system, 2) investigate the appropriate sensor modalities, 3) design the general architecture of the integrated system, and 4) evaluate the methods on real data for real-time feasibility and performance over state-of-the-art. We bring to the table an already existing database of data collected from UGVs with many state-of-the-art sensors, ready-made platforms for integrating any additional sensors identified by the sensor study and collecting data, complementary expertise in sensor technology, a software base of cutting-edge perception methods for the competitive analysis, and the machine learning experience and knowledge in the area of online and semi-supervised learning.

* Information listed above is at the time of submission. *

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