Abstract
This paper considers a learning process in which the student teaches the robot new tasks, such as lifting unknown weights, via reinforcement learning procedure. Using CAD software, we ran virtual trials using the robot’s digital twin in place of physical robot trials. When performing the task, the robot measures and sends the value of the weight to an IoT controller implemented on the ThingWorx platform and receives parameters of the optimal posture found through the virtual trials. When we presented the robot learning process to high school students they had difficulty fully understanding the robot’s dynamics and selection of posture parameters. To address this difficulty, we developed an augmented reality interface which allows students to visualize robot postures on the digital twin and monitor the change in parameters (such as the center of gravity) measured by virtual sensors. The student can select a weightlifting posture and control the robot to implement it.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Richert, A., Shehadeh, M., Plumanns, L., Grob, K., Schuster, K., and Sabina, J.: Educating engineers for Industry 4.0: virtual worlds and human-robot-teams. Empirical studies towards a new educational age. In: Proceedings of EDUCON, pp. 142–149 (2016)
Porter, M.E., Heppelmann, J.E.: How smart, connected products are transforming competition. Harvard Bus. Rev. 92(11), 64–88 (2014)
Porter, M.E., Heppelmann, J.E.: How smart, connected products are transforming companies. Harvard Bus. Rev. 93(10), 96–114 (2015)
Kanda, T., Hirano, T., Eaton, D., Ishiguro, H.: Interactive robots as social partners and peer tutors for children: a field trial. Hum. Comput. Interact. 19(1), 61–84 (2004)
Verner, I., Polishuk, A., Klein, Y., Cuperman, D., Mir, R.: A learning excellence program in a science museum as a pathway into robotics. Int. J. Eng. Educ. 28(3), 523–533 (2012)
Arumugam, S., Kalle, R.K., Prasad, A.R.: Wireless robotics: opportunities and challenges. Wirel. Pers. Commun. 70(3), 1033–1058 (2013)
Fiorella, L., Mayer, R.: The relative benefits of learning by teaching and teaching expectancy. Contemp. Educ. Psychol. 38, 281–288 (2013)
Okita, S.Y., Schwartz, D.L.: Learning by teaching human pupils and teachable agents: the importance of recursive feedback. J. Learn. Sci. 22(3), 375–412 (2013)
Harada, K., Kajita, S., Saito, H., Morisawa, M., Kanehiro, F., Fujiwara, K., Kaneko, K., Hirukawa, H.: A humanoid robot carrying a heavy object. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1712–1717 (2005)
Arisumi, H., Miossec, S., Chardonnet, J.R., Yokoi, K.: Dynamic lifting by whole body motion of humanoid robots. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 668–675 (2008)
Rosenstein, M.T., Barto, A.G., Van Emmerik, R.E.: Learning at the level of synergies for a robot weightlifter. Robot. Auton. Syst. 54(8), 706–717 (2006)
Ahlgren, D.J., Verner, I.M.: Socially responsible engineering education through assistive robotics projects: the RoboWaiter competition. Int. J. Social Robot. 5(1), 127–138 (2013)
Verner, I., Cuperman, D., Cuperman, A., Ahlgren, D., Petkovsek, S., Burca, V.: Humanoids at the Assistive Robot Competition RoboWaiter 2012. In: Robot Intelligence Technology and Applications 2012, pp. 763–774, Springer, Heidelberg (2013)
Verner, I., Cuperman, D., Krishnamachar, A., Green, S.: Learning with Learning Robots: A weight-lifting project. Robot Intelligence Technology and Applications, vol. 4, pp. 319–327. Springer, Berlin (2017)
Verner, I., Cuperman, D., Reitman, M.: Robot online learning to lift weights: a way to expose students to robotics and intelligent technologies. Int. J. Online Eng. 13(8), 174–182 (2017)
Porter, M.E., Heppelmann, J.E.: Why every organization needs an augmented reality strategy. Harvard Bus. Rev. 95(6), 46–57 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Verner, I., Reitman, M., Cuperman, D., Yan, T., Finkelstein, E., Romm, T. (2019). Exposing Robot Learning to Students in Augmented Reality Experience. In: Auer, M., Langmann, R. (eds) Smart Industry & Smart Education. REV 2018. Lecture Notes in Networks and Systems, vol 47. Springer, Cham. https://doi.org/10.1007/978-3-319-95678-7_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-95678-7_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-95677-0
Online ISBN: 978-3-319-95678-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)