C. Yang, K. Huang, H. Cheng, Y. Li, and C. Su, Haptic identification by ELM-controlled uncertain manipulator, Man, Cybern., Syst, vol.47, issue.8, pp.2398-2409, 2017.

C. Yang, C. Zeng, P. Liang, Z. Li, R. Li et al., Interface design of a physical human-robot interaction system for human impedance adaptive skill transfer, IEEE Trans. Autom. Sci. Eng, vol.15, issue.1, pp.329-340, 2018.

J. Huang, Y. Wang, and T. Fukuda, Set-membership-based fault detection and isolation for robotic assembly of electrical connectors, IEEE Trans. Autom. Sci. Eng, vol.15, issue.1, pp.160-171, 2018.

J. Huang, W. Huo, W. Xu, S. Mohammed, and Y. Amirat, Control of upper-limb power-assist exoskeleton using a human-robot interface based on motion intention recognition, IEEE Trans. Autom. Sci. Eng, vol.12, issue.4, pp.1257-1270, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01538507

H. Liu, Y. Wu, F. Sun, B. Fang, and D. Guo, Weakly paired multimodal fusion for object recognition, IEEE Trans. Autom. Sci. Eng, vol.15, issue.2, pp.784-795, 2018.

J. Su, H. Qiao, Z. Ou, and Z. Y. Liu, Vision-based caging grasps of polyhedron-like workpieces with a binary industrial gripper, IEEE Trans. Autom. Sci. Eng, vol.12, issue.3, pp.1033-1046, 2015.

L. Peternel, T. Petri?, and J. Babi?, Human-in-the-loop approach for teaching robot assembly tasks using impedance control interface, Proc. IEEE Int. Conf. Robot. Autom, pp.1497-1502, 2015.

H. Liu, F. Sun, B. Fang, and X. Zhang, Robotic room-level localization using multiple sets of sonar measurements, IEEE Trans. Instrum. Meas, vol.66, issue.1, pp.2-13, 2017.

J. Su, H. Qiao, C. Liu, Y. Song, and A. Yang, Grasping objects: The relationship between the cage and the form-closure grasp, IEEE Robot. Autom. Mag, vol.24, issue.3, pp.84-96, 2017.

Z. Liu, J. Luo, L. Wang, Y. Zhang, C. L. Chen et al., A time-sequence-based fuzzy support vector machine adaptive filter for tremor cancelling for microsurgery, Int. J. Syst. Sci, vol.46, issue.6, pp.1131-1146, 2015.

Z. Liu, C. Mao, J. Luo, Y. Zhang, and C. L. Chen, A threedomain fuzzy wavelet network filter using fuzzy PSO for robotic assisted minimally invasive surgery, Knowl.-Based Syst, vol.66, pp.13-27, 2014.

H. Liu, F. Sun, B. Fang, and S. Lu, Multimodal measurements fusion for surface material categorization, IEEE Trans. Instrum. Meas, vol.67, issue.2, pp.246-256, 2018.

R. Li and H. Qiao, Condition and strategy analysis for assembly based on attractive region in environment, IEEE/ASME Trans. Mechatronics, vol.22, issue.5, pp.2218-2228, 2017.

H. Liu, J. Qin, F. Sun, and D. Guo, Extreme kernel sparse learning for tactile object recognition, IEEE Trans. Cybern, vol.47, issue.12, pp.4509-4520, 2017.

R. Chalodhorn, D. B. Grimes, K. Grochow, and R. P. Rao, Learning to walk through imitation, Proc. IJCAI, vol.7, pp.2084-2090, 2007.

N. Koenig and M. J. Matari?, Robot life-long task learning from human demonstrations: A Bayesian approach, Auto. Robots, vol.41, issue.5, pp.1173-1188, 2017.

D. H. Grollman and O. C. Jenkins, Dogged learning for robots, Proc. IEEE Int. Conf. Robot. Autom, pp.2483-2488, 2007.

T. Inamura, N. Kojo, T. Sonoda, K. Sakamoto, K. Okada et al., Intent imitation using wearable motion capturing system with on-line teaching of task attention, Proc. 5th IEEE-RAS Int. Conf. Humanoid Robots, pp.469-474, 2005.

A. Hussein, M. M. Gaber, E. Elyan, and C. Jayne, Imitation learning: A survey of learning methods, ACM Comput. Surv, vol.50, issue.2, 2017.

M. Field, D. Stirling, Z. Pan, and F. Naghdy, Learning trajectories for robot programing by demonstration using a coordinated mixture of factor analyzers, IEEE Trans. Cybern, vol.46, issue.3, pp.706-717, 2016.

A. Vakanski, I. Mantegh, A. Irish, and F. Janabi-sharifi, Trajectory learning for robot programming by demonstration using hidden Markov model and dynamic time warping, Man, Cybern. B, Cybern, vol.42, issue.4, pp.1039-1052, 2012.

X. Yin and Q. Chen, Trajectory generation with spatio-temporal templates learned from demonstrations, IEEE Trans. Ind. Electron, vol.64, issue.4, pp.3442-3451, 2017.

M. Deni?a, A. Gams, A. Ude, and T. Petri?, Learning compliant movement primitives through demonstration and statistical generalization, IEEE/ASME Trans. Mechatronics, vol.21, issue.5, pp.2581-2594, 2016.

M. Racca, J. Pajarinen, A. Montebelli, and V. Kyrki, Learning incontact control strategies from demonstration, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp.688-695, 2016.

L. Rozo, P. Jiménez, and C. Torras, Force-based robot learning of pouring skills using parametric hidden Markov models, Proc. IEEE 9th Workshop Robot Motion Control (RoMoCo), pp.227-232, 2013.

A. K. Tanwani and S. Calinon, Learning robot manipulation tasks with task-parameterized semitied hidden semi-Markov model, IEEE Robot. Autom. Lett, vol.1, issue.1, pp.235-242, 2016.

A. K. Tanwani and S. Calinon, Small variance asymptotics for non-parametric online robot learning, 2016.

K. Khokar, R. Alqasemi, S. Sarkar, K. Reed, and R. Dubey, A novel telerobotic method for human-in-the-loop assisted grasping based on intention recognition, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp.4762-4769, 2014.

N. Stefanov, A. Peer, and M. Buss, Online intention recognition for computer-assisted teleoperation, Proc. IEEE Int. Conf. Robot. Autom. (ICRA), pp.5334-5339, 2010.

Z. Wang, Probabilistic movement modeling for intention inference in human-robot interaction, Int. J. Robot. Res, vol.32, issue.7, pp.841-858, 2013.

G. Maeda, G. Neumann, M. Ewerton, R. Lioutikov, and J. Peters, A probabilistic framework for semi-autonomous robots based on interaction primitives with phase estimation, Robotics Research, pp.253-268, 2018.

H. C. Ravichandar and A. P. Dani, Human intention inference using expectation-maximization algorithm with online model learning, IEEE Trans. Autom. Sci. Eng, vol.14, issue.2, pp.855-868, 2017.

L. Peternel, E. Oztop, and J. Babi?, A shared control method for online human-in-the-loop robot learning based on locally weighted regression, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp.3900-3906, 2016.

C. J. Pérez-del-pulgar, J. Smisek, V. F. Muñoz, and A. Schiele, Using learning from demonstration to generate real-time guidance for haptic shared control, Proc. IEEE Int. Conf. Syst., Man, Cybern, pp.3205-003210, 2016.

A. Pervez, A. Ali, J. Ryu, and D. Lee, Novel learning from demonstration approach for repetitive teleoperation tasks, Proc. IEEE World Haptics Conf, pp.60-65, 2017.

J. Su, Representation and inference of user intention for Internet robot, Man, Cybern., Syst, vol.44, issue.8, pp.995-1002, 2014.

C. Yang, J. Luo, Y. Pan, Z. Liu, and C. Su, Personalized variable gain control with tremor attenuation for robot teleoperation, Man, Cybern. A, Syst, vol.48, issue.10, pp.1759-1770, 2018.

G. R. Naik, S. E. Selvan, M. Gobbo, A. Acharyya, and H. T. Nguyen, Principal component analysis applied to surface electromyography: A comprehensive review, IEEE Access, vol.4, pp.4025-4037, 2016.

G. Biagetti, P. Crippa, S. Orcioni, and C. Turchetti, Homomorphic deconvolution for MUAP estimation from surface EMG signals, IEEE J. Biomed. Health Informat, vol.21, issue.2, pp.328-338, 2017.

L. Rozo, J. Silvério, S. Calinon, and D. G. Caldwell, Learning controllers for reactive and proactive behaviors in human-robot collaboration, Frontiers Robot. AI, vol.3, issue.30, pp.1-11, 2016.

A. K. Tanwani and S. Calinon, A generative model for intention recognition and manipulation assistance in teleoperation, Proc

. Ieee/rsj and . Int, Conf. Intell. Robots Syst. (IROS), pp.43-50, 2017.

S. Calinon, A. Pistillo, and D. G. Caldwell, Encoding the time and space constraints of a task in explicit-duration hidden Markov model, Proc. IEEE/RSJ Int. Conf. Intell. Robots Syst. (IROS), pp.3413-3418, 2011.

S. Calinon and A. Billard, Statistical learning by imitation of competing constraints in joint space and task space, Adv. Robot, vol.23, issue.15, pp.2059-2076, 2009.

C. Yang, X. Wang, Z. Li, Y. Li, and C. Su, Teleoperation control based on combination of wave variable and neural networks, Man, Cybern., Syst, vol.47, issue.8, pp.2125-2136, 2017.

C. Yang, Y. Jiang, Z. Li, W. He, and C. Su, Chenguang Yang (M'10-SM'16) received the B.Eng. degree in measurement and control from Northwestern Polytechnical University, IEEE Trans. Ind. Informat, vol.13, issue.3, pp.1162-1171, 2005.