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SBIR Phase I:Modular and Updatable Artificial Intelligence (AI) for Robotics

Award Information
Agency: National Science Foundation
Branch: N/A
Contract: 2127085
Agency Tracking Number: 2127085
Amount: $254,746.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: R
Solicitation Number: NSF 21-562
Timeline
Solicitation Year: 2021
Award Year: 2022
Award Start Date (Proposal Award Date): 2022-02-01
Award End Date (Contract End Date): 2023-12-31
Small Business Information
3168 SOUTH CT
PALO ALTO, CA 94306
United States
DUNS: 080374279
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Tsvi Achler
 (217) 333-2187
 achler@illinois.edu
Business Contact
 Tsvi Achler
Phone: (217) 333-2187
Email: achler@illinois.edu
Research Institution
N/A
Abstract

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to provide a novel recognition architecture to computer vision in the robotics industry. The project seeks to enable computer learn without rehearsal, allowing corrections for details that are present in the real world environment. The aim of this project is a solution to be used by computer vision customers to solve their problems immediately (without sending data back to retrain the whole network), reducing machine and customer downtime and disruption while increasingproductivity. The initial focus is on robotics with computer vision limitations though the technology may be useful to other industries. Success in improving computer vision-based learning could facilitate disaster responses, augment current physical abilities, and enable exploration beyond the boundaries of Earth.This Small Business Innovation Research (SBIR) Phase I project will help create a framework to overcome rehearsal requirements that limit automated robots’ utility within life-like, dynamic environments. Artificial intelligence (AI) remains inflexible compared to humans at quickly accumulating knowledge without forgetting what they have previously learned. Robots using AI are currently only used in environments that are very limited and are very tightly controlled. Everything that might happen in the robot’s work environment must be included their training set. The proposed AI solution is suited for learning in dynamic environments without rehearsal while maintaining scalability as information is encountered. This technology may allow robots to be trained within their environment. This project may enable visual capabilities leading to a demonstration of flexible learning without rehearsal within dynamic robotic environments.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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

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