Grant: Alliance Grants

Manufacturing is one of Canada's most important economic sectors. However, due to the highly dynamic nature of the manufacturing environment, most production processes are done by human operators as robots still cannot replicate human cognition and dexterity. To achieve desired quality control and reduce production time and cost, a reliable human action recognition system is highly desired for next-generation manufacturing. The project proposes to develop new machine learning algorithms that can work with unlabelled data or limited training data while achieving competing or even better performance than supervised learning models. Specifically, we propose to investigate (i) a self-supervised learning strategy; (ii) a multimodal learning model; (iii) an efficient online action recognition pipeline; and (iv) a lifelong learning paradigm for action detection and recognition with unlabelled data or limited training data while achieving competing or even better performance than supervised learning models.The project will collaborate with i-5O, a technology innovator for manufacturing using AI-powered vision systems. In addition to the cash contribution, i-5O has committed substantial in-kind contributions to this project. This project directly aligns with i-5O's strategic plan in AI and activities on improving their algorithms for ease of deployment and implementation. The proposed solution is technically novel and specifically designed to solve the problem of assembly action recognition. The proposed solutions will significantly reduce the model's dependency on training data and increase its efficiency and accuracy in assembly action recognition. Thus, it will greatly reduce the cost of data annotation and shorten the process of model training and development. The system developed in this project will help Canadian manufacturers improve their labor efficiency through accurate real-time monitoring of their production operations and stay competitive in the global manufacturing landscape.