Robotics, Commonsense Knowledge and Smart Manufacturing

This research focuses on AI and Robotics, more specifically the deployment of commonsense knowledge (CSK) and related aspects within the realm of collaborative robotics, i.e. human-robot collaboration (HRC) with particular emphasis on the Smart Manufacturing domain. This entails learning from CSK sources online and also developing knowledge bases (KBs) entailing fundamental CSK as applicable to several robotics tasks, especially in HRC. It involves mathematical modeling, algorithm development and design of HRC tasks based on the CSK concepts along with experimentation via simulation studies as well as real robotics laboratory experiments. An important approach proposed here includes CSK-based-HRC for robot task planning and optimization in smart manufacturing pioneered by us and published at IEEE Big Data 2020 and IEEE IEMTRONICS 2020 conferences with a best paper award in the Robotics Track at the IEEE IEMTRONICS 2020 conference. Much experimentation so far includes automobile manufacturing using small-scale robotics systems ans simulations in the context of HRC. Other important tasks in this effort include harnessing search methods with and without heuristics in the context of robotic motion planning, considering issues such as trust in robotics and enhancing the CSK-based-HRC approach for further optimization.

• M.S. Thesis Student: Christopher J. Conti (Graduated Jan 2021)
• M.S. AI Course Student: Laura Paulino (ongoing)
• M.S. AI Course Student: Correy Hannum (Graduated Jan 2021)
• Co-author on Papers: Dr. Weitian Wang (CS, Montclair)
• Funding: TA from Computer Science; Summer Research Grant (2020)