Robot learning from demonstrations: Emulation learning in environments with moving obstacles

In this paper, we present an approach to the problem of Robot Learning from Demonstration (RLfD) in a dynamic environment, i.e. an environment whose state changes throughout the course of performing a task. RLfD mostly has been successfully exploited only in non-varying environments to reduce the programming time and cost, e.g. fixed manufacturing workspaces. Non-conventional production lines necessitate Human–Robot Collaboration (HRC) implying robots and humans must work in shared workspaces. In such conditions, the robot needs to avoid colliding with the objects that are moved by humans in the workspace. Therefore, not only is the robot: (i) required to learn a task model from demonstrations; but also, (ii) must learn a control policy to avoid a stationary obstacle. Furthermore, (iii) it needs to build a control policy from demonstration to avoid moving obstacles. Here, we present an incremental approach to RLfD addressing all these three problems. We demonstrate the effectiveness of the proposed RLfD approach, by a series of pick-and-place experiments by an ABB YuMi robot. The experimental results show that a person can work in a workspace shared with a robot where the robot successfully avoids colliding with him.