2000 character limit reached
TeachingBot: Robot Teacher for Human Handwriting
Published 21 Sep 2023 in cs.RO | (2309.11848v1)
Abstract: Teaching physical skills to humans requires one-on-one interaction between the teacher and the learner. With a shortage of human teachers, such a teaching mode faces the challenge of scaling up. Robots, with their replicable nature and physical capabilities, offer a solution. In this work, we present TeachingBot, a robotic system designed for teaching handwriting to human learners. We tackle two primary challenges in this teaching task: the adaptation to each learner's unique style and the creation of an engaging learning experience. TeachingBot captures the learner's style using a probabilistic learning approach based on the learner's handwriting. Then, based on the learned style, it provides physical guidance to human learners with variable impedance to make the learning experience engaging. Results from human-subject experiments based on 15 human subjects support the effectiveness of TeachingBot, demonstrating improved human learning outcomes compared to baseline methods. Additionally, we illustrate how TeachingBot customizes its teaching approach for individual learners, leading to enhanced overall engagement and effectiveness.
- D. P. Losey, C. G. McDonald, E. Battaglia, and M. K. O’Malley, “A review of intent detection, arbitration, and communication aspects of shared control for physical human–robot interaction,” Applied Mechanics Reviews, 2018.
- M. Arduengo, A. Colomé, J. Borràs, L. Sentis, and C. Torras, “Task-adaptive robot learning from demonstration with gaussian process models under replication,” IEEE Robotics and Automation Letters, 2021.
- Y. Zhou, J. Gao, and T. Asfour, “Learning via-point movement primitives with inter-and extrapolation capabilities,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019.
- Y. Li, K. P. Tee, W. L. Chan, R. Yan, Y. Chua, and D. K. Limbu, “Continuous role adaptation for human–robot shared control,” IEEE Transactions on Robotics, vol. 31, no. 3, pp. 672–681, 2015.
- A. U. Pehlivan, D. P. Losey, and M. K. O’Malley, “Minimal assist-as-needed controller for upper limb robotic rehabilitation,” IEEE Transactions on Robotics, 2015.
- D. P. Losey, H. J. Jeon, M. Li, K. Srinivasan, A. Mandlekar, A. Garg, J. Bohg, and D. Sadigh, “Learning latent actions to control assistive robots,” Autonomous robots, vol. 46, no. 1, pp. 115–147, 2022.
- C. Yu, Y. Xu, L. Li, and D. Hsu, “Coach: Cooperative robot teaching,” in Proceedings of The 6th Conference on Robot Learning, 2023.
- N. Jaquier, D. Ginsbourger, and S. Calinon, “Learning from demonstration with model-based gaussian process,” in Conference on Robot Learning, 2020.
- X. Li, Y.-H. Liu, and H. Yu, “Iterative learning impedance control for rehabilitation robots driven by series elastic actuators,” Automatica, 2018.
- L. Pezeshki, H. Sadeghian, M. Keshmiri, X. Chen, and S. Haddadin, “Cooperative assist-as-needed control for robotic rehabilitation: A two-player game approach,” IEEE Robotics and Automation Letters, vol. 8, no. 5, pp. 2852–2859, 2023.
- Z. Yang, S. Guo, Y. Liu, M. Kawanishi, and H. Hirata, “A task performance-based semg-driven variable stiffness control strategy for upper limb bilateral rehabilitation system,” IEEE/ASME Transactions on Mechatronics, 2022.
- A. Singla, I. Bogunovic, G. Bartók, A. Karbasi, and A. Krause, “Near-optimally teaching the crowd to classify,” International Conference on Machine Learning, 2014.
- S. Zilles, S. Lange, R. Holte, and M. Zinkevich, “Models of cooperative teaching and learning,” Journal of Machine Learning Research, 2011.
- T. Doliwa, H. U. Simon, and S. Zilles, “Recursive teaching dimension, learning complexity, and maximum classes,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2010.
- O. Mac Aodha, S. Su, Y. Chen, P. Perona, and Y. Yue, “Teaching categories to human learners with visual explanations,” in IEEE Conference on Computer Vision and Pattern Recognition, 2018.
- M. Srivastava, E. Biyik, S. Mirchandani, N. Goodman, and D. Sadigh, “Assistive teaching of motor control tasks to humans,” in Advances in Neural Information Processing Systems, 2022.
- M. Srivastava, N. Goodman, and D. Sadigh, “Generating language corrections for teaching physical control tasks,” in 40th International Conference on Machine Learning (ICML), 2023.
- R. Tian, M. Tomizuka, A. D. Dragan, and A. V. Bajcsy, “Towards modeling and influencing the dynamics of human learning,” Proceedings of the 2023 ACM/IEEE International Conference on Human-Robot Interaction, 2023.
- A. N. Rafferty, E. Brunskill, T. L. Griffiths, and P. Shafto, “Faster teaching by pomdp planning,” in Artificial Intelligence in Education, 2011.
- Y. Huang, J. Silvério, L. Rozo, and D. G. Caldwell, “Generalized task-parameterized skill learning,” in 2018 IEEE international conference on robotics and automation (ICRA). IEEE, 2018, pp. 5667–5474.
- Y. Huang, L. Rozo, J. Silvério, and D. G. Caldwell, “Kernelized movement primitives,” The International Journal of Robotics Research, 2019.
- P. Pastor, H. Hoffmann, T. Asfour, and S. Schaal, “Learning and generalization of motor skills by learning from demonstration,” in 2009 IEEE International Conference on Robotics and Automation, 2009.
- A. Paraschos, C. Daniel, J. Peters, and G. Neumann, “Using probabilistic movement primitives in robotics,” Autonomous Robots, 2018.
- C. Zou, R. Huang, H. Cheng, and J. Qiu, “Learning gait models with varying walking speeds,” IEEE Robotics and Automation Letters, 2020.
- H. Wu, Z. Xu, W. Yan, Y. Ou, Z. Liao, and X. Zhou, “A framework of rehabilitation-assisted robot skill representation, learning, and modulation via manifold-mappings and gaussian processes,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.
- M. Sharifi, A. Zakerimanesh, J. K. Mehr, A. Torabi, V. K. Mushahwar, and M. Tavakoli, “Impedance variation and learning strategies in human-robot interaction,” IEEE Transactions on Cybernetics, 2021.
- Z. Warraich and J. A. Kleim, “Neural plasticity: the biological substrate for neurorehabilitation,” Pm&r, 2010.
- E. Caldarelli, A. Colomé, and C. Torras, “Perturbation-based stiffness inference in variable impedance control,” IEEE Robotics and Automation Letters, vol. 7, no. 4, pp. 8823–8830, 2022.
- Y. Li, X. Zhou, J. Zhong, and X. Li, “Robotic impedance learning for robot-assisted physical training,” Frontiers in Robotics and AI, 2019.
- M. Maaref, A. Rezazadeh, K. Shamaei, R. Ocampo, and T. Mahdi, “A bicycle cranking model for assist-as-needed robotic rehabilitation therapy using learning from demonstration,” IEEE Robotics and Automation Letters, vol. 1, no. 2, pp. 653–660, 2016.
- S. Pareek, H. J. Nisar, and T. Kesavadas, “Ar3n: A reinforcement learning-based assist-as-needed controller for robotic rehabilitation,” IEEE Robotics & Automation Magazine, 2023.
- Z. Hou, W. Yang, R. Chen, P. Feng, and J. Xu, “A hierarchical compliance-based contextual policy search for robotic manipulation tasks with multiple objectives,” IEEE Transactions on Industrial Informatics, vol. 19, no. 4, pp. 5444–5455, 2022.
- B. Clement, D. Roy, P.-Y. Oudeyer, and M. Lopes, “Multi-armed bandits for intelligent tutoring systems,” in Journal of Educational Data Mining, 2015.
- Y. Huang, F. J. Abu-Dakka, J. Silvério, and D. G. Caldwell, “Toward orientation learning and adaptation in cartesian space,” IEEE Transactions on Robotics, 2020.
- X. Zhang, L. Sun, Z. Kuang, and M. Tomizuka, “Learning variable impedance control via inverse reinforcement learning for force-related tasks,” IEEE Robotics and Automation Letters, 2021.
- S. Hu, R. Mendonca, M. J. Johnson, and K. J. Kuchenbecker, “Robotics for occupational therapy: Learning upper-limb exercises from demonstrations,” IEEE Robotics and Automation Letters, 2021.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.
