An Approach to Acquire Path-Following Skills by Industrial Robots From Human Demonstration

• Published in: IEEE access 2021, Vol.9, p.82351-82363
• Main Authors: Rodriguez-Linan, Angel, Lopez-Juarez, Ismael, Maldonado-Ramirez, Alan, Zalapa-Elias, Antonio, Torres-Trevino, Luis, Navarro-Gonzalez, Jose Luis, Chinas-Sanchez, Pamela
• Format: Article
• Language: English
• Subjects: 3D trajectory acquisition; Estimation; Human motion; Industrial robots; inertial measurement unit (IMU); Inertial platforms; Learning; Learning from demonstration (LfD); Motion capture; Robot kinematics; Robot sensing systems; Robotics; Robots; Sensors; Service robots; Task analysis; Teach pendants
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3086701

Industrial robots have mainly been programmed by operators using teach pendants in a point-to-point scheme with limited sensing capabilities. New developments in robotics have attracted a lot of attention to robot motor skill learning via human interaction using Learning from Demonstration (LfD) techniques. Robot skill acquisition using LfD techniques is characterised by a high-level stage in charge of learning connected actions and a low-level stage concerned with motor coordination and reproduction of an observed path. In this paper, we present an approach to acquire a path-following skill by a robot in the low-level stage which deals with the correspondence of mapping links and joints from a human operator to a robot so that the robot can actually follow a path. We present the design of an Inertial Measurement Unit (IMU) device that is primarily used as an input to acquire the robot skill. The approach is validated using a motion capture system as ground truth to assess the spatial deviation from the human-taught path to the robot's final trajectory.