Research

Project PI: Weitian Wang

Project Duration: February 2022 - January 2024

Project Description:

​Robotics is one of critical technologies in advancing the manufacturing industry, because of its potential to heighten the efficiency in the productivity and part quality. Traditional industrial robots are fenced off from human workers on production lines; on the contrary, collaborative robots are not, making them capable of democratizing manufacturing industries with dynamic customer demands and high flexibility. This project will develop a teaching-learning-prediction-collaboration framework for robots to proactively learn from human demonstrations, predict human intentions, and collaborate with humans in collaborative manufacturing tasks. The major questions to be solved include the following: (i) how can a human-robot collaborative manufacturing process be mathematically described and can robots learn task knowledge from human demonstrations, (ii) how can robots assist human partners based on the prediction of human intentions in the collaboration process, and (iii) how can the framework be validated in human-robot collaborative manufacturing tasks? To fill the knowledge gaps, the human-robot collaboration will be parameterized through a Markov Decision Process and develop a multimodal-information-based approach for robots to learn task customization and human working preference from human partners’ demonstrations in collaborative manufacturing environments. Further, computational human intention prediction and human-robot collaboration models will be developed for robots to leverage the learned strategies to proactively predict human partners’ upcoming intentions and assist humans in shared tasks. Moreover, user studies will be conducted to evaluate the effectiveness of the approaches in collaboration quality improvement by applying findings to real-world human-robot collaborative tasks in advanced manufacturing contexts. This project will offer students at Montclair State University, which has a diverse student body from underrepresented groups, with the latest robotics training and research, which will diversify the future workforce and potentially benefit the US industry. In addition, this project will launch robotics workshops with cutting-edge hands-on activities for local K-12 schools, especially from underserved districts.

Publications:

• O. Obidat, J. Parron, R. Li, J. Rodano, W. Wang*, "Development of a Teaching- Learning-Prediction-Collaboration Model for Human-Robot Collaborative Tasks," in Proc. The 13th IEEE International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, 2023, pp. 1-6.

• J. Rodano, O. Obidat, J. Parron, R. Li, M. Zhu, W. Wang*, "Teaching Humanoid Robots to Assist Humans for Collaborative Tasks," in Proc. IEEE International Conference on Smart Computing, 2023, pp. 1-5.

• T. Nguyen, J. Parron, O. Obidat, A. Tuininga, W. Wang*, "Ready or Not? A Robot-Assisted Crop Harvest Solution in Smart Agriculture Contexts," in Proc. IEEE International Conference on Smart Computing, 2023, pp. 1-6.

• L. Rodriguez, Z. Przedworska, O. Obidat, J. Parron, and W. Wang*, "Development and Implementation of an AI-Embedded and ROS-Compatible Smart Glove System in Human-Robot Interaction," in Proc. The 19th IEEE International Conference on Mobile Ad-Hoc and Smart Systems Conference (IEEE MASS), 2022, pp. 1-6. (NSF-RMBL REU Travel Award)

• A. Coutras, O. Obidat, M. Zhu, and W. Wang*, "JUST TELL ME: A Robot-assisted E-health Solution for People with Lower-extremity Disability," in Proc. The 9th International Conference on Automation, Robotics and Applications (ICARA 2023), 2023, pp. 1-5.