Robot learning of industrial assembly task via human demonstrations

Abstract

Human–robot collaboration in industrial applications is a challenging robotic task. Human working together with the robot at a workplace to complete a task may create unpredicted events for the robot, as humans can act unpredictably. Humans tend to perform a task in a not fully repetitive manner using their expertise and cognitive capabilities. The traditional robot programming cannot cope with these challenges of human–robot collaboration. In this paper, a framework for robot learning by multiple human demonstrations is introduced. Through the demonstrations, the robot learns the sequence of actions for an assembly task (high-level learning) without the need of pre-programming. Additionally, the robot learns every path as needed for object manipulation (low-level learning). Once the robot has the knowledge of the demonstrated task, it can perform the task in collaboration with the human. However, the need for adaptation of the learned knowledge may arise as the human collaborator could introduce changes in the environment, such as placing an object to be manipulated in a position and orientation different from the demonstrated ones. In this paper, a novel real-time adaptation algorithm to cope with these changes in the environment, introduced by the human factor, is proposed. The proposed algorithm is able to identify the sequence of actions needed to be performed in a new environment. A Gaussian Mixture Model-based modification algorithm is able to adapt the learned path in order to enable robot to successfully complete the task without the need of additional training by demonstration. The proposed framework copes with changes in the position and orientation of the objects to be manipulated and also provides obstacle avoidance. Moreover, the framework enables the human collaborator to suggest different sequence of actions for the learned task, which will be performed by the robot. The proposed algorithm was tested on a dual-arm industrial robot in an assembly scenario and the results are presented. Shown results demonstrate a potential of the proposed robot learning framework to enable continuous human–robot collaboration.