Prof. Andrew Goldenberg
Full Professor
CEO, Anviv Mechatronics Inc.
Professor Emeritus, University of Toronto
golden@mie.utoronto.ca
Biography

Dr. Goldenberg is the founder of the field of Robotics at University of Toronto where he has been since 1982 as a Professor of Mechanical and Industrial Engineering, cross appointed in the Institute of Biomaterials & Biomedical Engineering and the Electrical and Computer Engineering. Since 2011 he has been a Professor Emeritus. Dr. Goldenberg is also an Adjunct Professor at Ryerson University and Guest Professor at Nanjing University of Science and Technology, P.R. China.

Dr. Goldenberg has supervised to-date the largest number of graduate students in the Faculty of Applied Science and Engineering (46 PhD and 64 MASc). He has an exceptional publication record with over 8300 citations (128 archival journal papers, 294 papers in major conferences, 15 book chapters and 105 patents granted and applied).

From 1975 – 1981 Dr. Goldenberg has been an employee of SPAR Aerospace Ltd., of Toronto, working on the development of the first Space Shuttle Remote Manipulator System (Canadarm).

Dr. Goldenberg is the founder and now former President of Engineering Services Inc. (ESI) – www.esit.com, established in 1982. ESI is a high-technology company involved in the development of robotics-based automation and technology. Under his leadership the company has achieved significant growth and a global leading role in a wide range of industrial sectors. From 2000 – 2001 Dr. Goldenberg was also the President of Virtek Engineering Science Inc. (VESI), a high-technology company formed with the acquisition of part of ESI by Virtek Vision International Ltd., a company listed publicly. Dr. Goldenberg is also President of ANVIV Mechatronics Inc. (AMI), which he founded in 2006. ANVIV is a high-technology company involved in the development of mechatronics products. In May 2015 ESI has been acquired by a Chinese consortium located in Shenzhen, P.R. China. Dr. Goldenberg has continued to be the President of ESI after the acquisition until the Chinese consortium became a public company in November 2016 listed in Hong Kong and Dr. Goldenberg was appointed as Chief Technology Officer of the public company. He terminated this appointment on May 12, 2019.

As of May 2019, Dr. Goldenberg has returned to the University on part-time basis to work on graduate research in the use of Artificial Intelligence in advanced Robotics, focusing on Personal Service Robots. He also continues his business activities in several ventures in this domain through ANVIV Mechatronics Inc., the company he founded in 2006.

Dr. Goldenberg is a Life Fellow of the Institute of Electrical and Electronics Engineers, Inc. (IEEE), a Fellow of the American Society of Mechanical Engineers (ASME), a Fellow of the Engineering Institute of Canada (EIC), a Fellow of the Canadian Academy of Engineering (CAE), a Fellow of The American Association for the Advancement of Science (AAAS), a Member of the Professional Engineers of Ontario (PEng), and a Designated Consulting Engineer in Ontario. He is the recipient of the 2010 PEO Engineering Medal for Entrepreneurship and the 2013 EIC Sir John Kennedy Medal for Outstanding Merit in the Engineering Profession.

Dr. Goldenberg is a former editor of the archival international journal IEEE Transactions on Robotics and Automation, and a member of the editorial boards of Robotica, Robotics in Japan, Journal of Robotics, Robotics Journal, Scientific World Journal, Industrial Engineering and Management Journal, SOJ Robotics and Automation, International Journal of Automation and Computing and others.

Dr. Goldenberg obtained his PhD in 1976 from the University of Toronto, and his MSc and BSc degrees from the Technion, Israel Institute of Technology, in 1969 and 1972, respectively. Dr. Goldenberg was born in Bucharest, Romania.

Speech Title: Bridging Between AI and Robotics for Business and Product Development

Abstract

Contemporary control systems methodologies in Robotics have their origin in the classical control system theory. With the exceptions of increasing adoption of modern computers in the hardware implementations, the design and analysis has been limited to the original knowhow. Most advances steamed from the computer hardware and related software and sensors.
As long as the robotics field included operations in mostly structured environments there was limited need to explore complexity beyond the standard requirements of high speed and high repeatability. These along with the payload capacity, robot weight and power requirements provided the necessary specifications that could be met for most applications.
At some point in time the concerns of developers shifted to the end-effector in form of generic hands that can be used for a multitude of tasks. In this context light weight and associated compliance of end-effectors became an issue. Nonetheless, the known methodologies of control covered most of the additional requirements.
The situation started to change when the required tasks to be performed became complex and variable, environments became unstructured and with human presence and the scope of the performance not fully known. This was brought up by the emergence of mobile robot and collaborative arms applications. It gave rise to requirements that could not be satisfied with the contemporary control systems methodologies.
As a result, a new trust emerged, that of Artificial Intelligence. It provides tools to deal with situations that are basically complex, involving humans and are performed in variable environments that are not fully known. There is a need and an opportunity to fuse fundamental AI techniques with established robot control methods involving mobility, image processing, structural compliance, and modularity to be used in controlling robot systems operating in complex situations.
There is a need to look at this quest from the critical point of view that currently AI serves well when the systems can be a priori trained or used in non-real time applications, ex. e-commerce, as opposed to robotics that are requiring real-time methods, which may pose limitations in the current use of AI.
The presentation will address some of the issues pertinent to the technology of AI for use in robots operating in complex environments.