Incremental Learning for Robot Sensing and Control

Award Information
Agency: Department of Defense
Branch: Army
Contract: W56HZV-13-C-0014
Agency Tracking Number: A2-5015
Amount: $407,770.00
Phase: Phase II
Program: STTR
Awards Year: 2013
Solicitation Year: 2009
Solicitation Topic Code: A09A-T030
Solicitation Number: 2009.A
Small Business Information
Net-Scale Technologies, Inc.
281 State Highway 79, Morganville, NJ, -
DUNS: 126637755
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Urs Muller
 President and CEO
 (732) 970-1441
Business Contact
 Paula Muller
Title: Vice President
Phone: (732) 970-1441
Research Institution
 New York University
 Yann Lecun
 715 Broadway
New York, NY, 10004-
 (212) 998-3283
 Nonprofit college or university
The purpose of this proposal is to build a working prototype of a highly-adaptive, vehicle-independent, compact, low-power, low-cost, autonomous ground robot navigation system that incorporates the results obtained in our Phase I effort and in our earlier DARPA LAGR (Learning Applied to Ground Robots) work. The system will be able to quickly and automatically adapt to changing environments in real time. Near-to-far learning techniques provide sensing far beyond stereo and LIDAR range, and deep learning techniques allow terrain classification, people detection, and the ability to automatically learn from the robot's own experience and from observations of human drivers (in semi-autonomous mode). We will show the prototype's readiness for commercial use by demonstrating its capabilities on at least two different vehicle platforms in realistic outdoor settings using military-relevant use cases. The system will be independent of any particular robot platform and will be capable of operating both self-sufficiently, relying only on its built-in sensors, or in an integrated unit with existing on-board sensors. Our system is designed to fully operate with passive vision-based sensors alone but its performance can be enhanced with additional sensor input, if available, such as LIDAR.

* information listed above is at the time of submission.

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