Robot Online Learning to Lift Weights: A Way to Expose Students to Robotics and Intelligent Technologies

Abstract

During interaction with learning robots the students are often faced with the challenge of understanding the robot intent and its practical realization. To answer this challenge, we propose a connected environment which integrates the robot, its digital twin and virtual sensors. We implemented a reinforcement learning scenario in which a humanoid robot learns to lift a weight of unknown mass through autonomous trial-and-error search. To expedite the process, trials of the physical robot are substituted by simulations with its digital twin. The optimal parameters of the robot posture for executing the weightlifting task, found by analysis of the virtual trials, are transmitted to the robot through internet communication. The approach exposes students to the concepts and technologies of machine learning, parametric design, digital prototyping and simulation, connectivity and internet of things. Pilot implementation of the approach indicates its potential for teaching freshman and HS students, and for teacher education.