Neural Network Controller For Adaptive Hand Eye             Coordination in Robots
{'Code': '15030000', 'Directorate': {'Abbreviation': 'TIP', 'LongName': 'Dir for Tech, Innovation, & Partnerships'}, 'Division': {'Abbreviation': 'TI', 'LongName': 'Translational Impacts'}}
{'SignBlockName': 'Ritchie B. Coryell', 'PO_EMAI': None, 'PO_PHON': None}
The biggest challenges facing robotics today are issues                    dealing with novelty, processor faults and self-organization.                   To meet these needs, the proposed study will implement a                        prototype of a neural network model used to adaptively control                  the coordination of a multijoint robot arm and two stereo                       cameras.  The implementation will be based on the computer                      simulations of an existing neural network model of adaptive                     hand-eye coordination.  The implementation will evolve smoothly                 in two generations:  the first, with commercially available                     equipment and the second, with new dedicated hardware.  In                      phase I of the SBIR work, the implementation will be designed                   for a combination of image processing hardware and a robot arm.                                                                                                 When fully implemented on dedicated hardware in SBIR phase II,                  this prototype system will coordinate a multi-joint robot arm                   to adaptively reach targets in three dimensions in real time.                        The neural system self-organizes and maintains visual-                     motor calibrations starting with only loosely defined                           relationships.  The system tolerates internal noise, partial                    system damage and changes in the mechanical and optical                         parameters of the robot as they occur during wear.  This                        adaptability will replace operator calibration in many                          different robots.
{'FirstName': 'Michael', 'LastName': 'Kuperstein', 'PI_MID_INIT': None, 'PI_SUFX_NAME': None, 'PI_FULL_NAME': 'Michael Kuperstein', 'EmailAddress': 'michael@metaphorivr.com', 'PI_PHON': '8009790480', 'NSF_ID': '000112455', 'StartDate': '02/01/1988', 'EndDate': None, 'RoleCode': 'Principal Investigator'}
{'Name': 'Neurogen', 'CityName': 'Brookline', 'ZipCode': '021462928', 'PhoneNumber': '6177392215', 'StreetAddress': '325 Harvard St., Suite 211', 'StreetAddress2': None, 'CountryName': 'United States', 'StateName': 'Massachusetts', 'StateCode': 'MA', 'CONGRESSDISTRICT': '04', 'CONGRESS_DISTRICT_ORG': 'MA04', 'ORG_UEI_NUM': None, 'ORG_LGL_BUS_NAME': None, 'ORG_PRNT_UEI_NUM': None}
{'Name': None, 'CityName': None, 'StateCode': None, 'ZipCode': None, 'StreetAddress': None, 'CountryCode': None, 'CountryName': None, 'StateName': None, 'CountryFlag': None, 'CONGRESSDISTRICT': None, 'CONGRESS_DISTRICT_PERF': '""'}
{'Code': '5371', 'Text': 'SBIR Phase I'}
{'url': 'https://www.nsf.gov/awardsearch/download?DownloadFileName=1987&All=true', 'xml': '8760112.xml'}