Towards Cognitive Human-Robot Collaboration: A Cognitive Architecture for Integrating Control, Learning, and Machine Vision

Human-robot collaboration in manufacturing settings requires robots to exhibit seamless human-like actions. To achieve this ultimate goal, robots need to be able to imitate human movements as well as generalise learned behaviours to novel scenarios. In this work, we propose a layered cognitive architecture to orchestrate the process of collaborative robots (cobots) learning from human guidance, reproducing the manipulation skills with the visual feedback in assembly tasks. This architecture is composed of somatic, reactive, adaptive, and contextual layers, enabling the robot to acquire skills through demonstration and adaptively generate new motion trajectories. In the learning phase, the force-torque sensor captures the human’s kinesthetic guidance, which the admittance controller transforms into the robot’s movements. The demonstrated movements are generalised into a motion model using Gaussian Mixture Models (GMMs). In the reproduction phase, “unseen” trajectories are generated by recalling the GMM associated with the target point. This approach is validated in a pilot human robot collaborative assembly task using the UR3 robot. The results demonstrate the system’s capability to generate unseen trajectories, adapt to varying object locations and different action sequences. The performance of the admittance controller during the learning phase is assessed, and the trajectory reproduced in the execution phase is presented. This work demonstrates the potential of the proposed framework for human-robot collaboration tasks.