M. A. Goodrich and A. C. Schultz, Human-robot interaction: a survey, Foundations and Trends R in Human-Computer Interaction, vol.1, issue.3, pp.203-275, 2008.

T. B. Sheridan, Human-robot interaction: status and challenges, Human factors, vol.58, issue.4, pp.525-532, 2016.

J. Luo, C. Yang, Q. Li, and M. Wang, A task learning mechanism for the telerobots, International Journal of Humanoid Robotics, 2019.

G. Tonietti, R. Schiavi, and A. Bicchi, Design and control of a variable stiffness actuator for safe and fast physical human/robot interaction, Proceedings of the 2005 IEEE international conference on robotics and automation, pp.526-531, 2005.

P. A. Lasota, T. Fong, and J. A. Shah, A survey of methods for safe human-robot interaction, Foundations and Trends R in Robotics, vol.5, issue.4, pp.261-349, 2017.

F. Ficuciello, L. Villani, and B. Siciliano, Impedance control of redundant manipulators for safe human-robot collaboration, Acta Polytechnica Hungarica, vol.13, issue.1, pp.223-238, 2016.

D. Surdilovic, Contact stability issues in position based impedance control: Theory and experiments, Proceedings of IEEE International Conference on Robotics and Automation, vol.2, pp.1675-1680, 1996.

K. H. Lee, S. G. Baek, H. J. Lee, H. R. Choi, H. Moon et al., Improving transparency in physical human-robot interaction using an impedance compensator, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.3591-3596, 2017.

J. Luo, C. Yang, N. Wang, and M. Wang, Enhanced teleoperation performance using hybrid control and virtual fixture, International Journal of Systems Science, vol.50, issue.3, pp.451-462, 2019.

Y. Li, K. P. Tee, W. L. Chan, R. Yan, Y. Chua et al., Continuous role adaptation for human-robot shared control, IEEE Transactions on Robotics, vol.31, issue.3, pp.672-681, 2015.

W. He, Z. Li, and C. P. Chen, A survey of human-centered intelligent robots: issues and challenges, IEEE/CAA Journal of Automatica Sinica, vol.4, issue.4, pp.602-609, 2017.

M. Khoramshahi and A. Billard, Intention-based motion adaptation using dynamical systems with human in the loop, vol.4, 2018.

J. R. Medina, S. Endo, and S. Hirche, Impedance-based gaussian processes for predicting human behavior during physical interaction, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.3055-3061, 2016.

J. Hua and S. Liao, A method for synchronously predicting human intention based on posture and force, 2018 7th International Conference on Sustainable Energy and Environment Engineering (ICSEEE 2018), 2018.

I. Batzianoulis, S. El-khoury, E. Pirondini, M. Coscia, S. Micera et al., Emg-based decoding of grasp gestures in reaching-tograsping motions, Robotics and Autonomous Systems, vol.91, pp.59-70, 2017.

K. Park and S. Lee, Movement intention decoding based on deep learning for multiuser myoelectric interfaces, 2016 4th International Winter Conference on Brain-Computer Interface (BCI), pp.1-2, 2016.

W. Wang, R. Li, Y. Chen, and Y. Jia, Human intention prediction in human-robot collaborative tasks, 2018.

, ACM/IEEE International Conference on Human-Robot Interaction, pp.279-280, 2018.

L. Liu, L. Chien, S. Pan, J. Ren, C. Chiao et al., Interactive torque controller with electromyography intention prediction implemented on exoskeleton robot ntuh-ii, 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp.1485-1490, 2017.

C. Cui, G. Bian, Z. Hou, J. Zhao, and H. Zhou, A multimodal framework based on integration of cortical and muscular activities for decoding human intentions about lower limb motions, IEEE transactions on biomedical circuits and systems, vol.11, issue.4, pp.889-899, 2017.

L. Peternel, N. Tsagarakis, and A. Ajoudani, Towards multi-modal intention interfaces for human-robot co-manipulation, 2016 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp.2663-2669, 2016.

C. P. Chen and Z. Liu, Broad learning system: An effective and efficient incremental learning system without the need for deep architecture, IEEE transactions on neural networks and learning systems, vol.29, pp.10-24, 2018.

C. P. Chen, Z. Liu, and S. Feng, Universal approximation capability of broad learning system and its structural variations, IEEE transactions on neural networks and learning systems, pp.1-14, 2018.

S. Feng and C. P. Chen, Fuzzy broad learning system: a novel neurofuzzy model for regression and classification, IEEE transactions on cybernetics, issue.99, pp.1-11, 2018.