Human-Robot Collaboration for Flexible Manufacturing

As manufacturers of high complexity, low volume products with considerable variation in build standards, one obstacle to our adoption of widespread robotics in the manufacturing process is the amount of programming required - it is currently difficult to see sufficient return on investment. The ability for the robot to understand what is required during collaboration with people to produce a solution autonomously and via imitation learning, would drastically reduce programming times, resulting in agile, reconfigurable manufacturing.

Current robotics and AI research on human-robot interaction and collaboration between people and cobots (collaborative robots) has led to the design of machine learning models of intention recognition, e.g. for the prediction of the goal of a cooperative task (e.g. Vinanzi et al. 2019) and for action learning via kinaesthetic imitation and linguistic instruction (Zhong et al. 2019). These models have been developed for simplified joint task scenarios, in laboratory settings. The latest advances in AI (e.g. deep learning; Sunderhauf et al. 2018) offer a timely opportunity to scale up intentional reading models for collaboration in realistic, industry setting scenarios. This will lead to the system understanding of its environment and the components with which it is required to interact, using a combination of visual and action recognition AI methods and interrogation of CAD models/digital twin.

PhD research tasks:
1. Scope of the literature on machine learning for intention reading and imitation learning and selection of human-robot joint task and of AI methods
2. Training dataset generation for the human-robot cooperation and learning task
3. Machine learning simulations on dataset and a valuation in human-robot interaction experiments

The project is directly aligned with EPSRC's priority areas in Robotics and AI.