[1]
Peer A., Einenkel S., and Buss M., Multi-fingered telemanipulation-mapping of a human hand to a three- finger gripper, in Proc. 17th IEEE Int. Symp. on Robot and Human Interactive Communication, Munich, Germany, 2008, pp. 465-470.
[2]
Rosell J., Suárez R., Rosales C., and Pérez A., Autonomous motion planning of a hand-arm robotic system based on captured human-like hand postures, Autonom. Rob., vol. 31, no. 1, pp. 87-102, 2011.
[3]
Pao L. and Speeter T. H., Transformation of human hand positions for robotic hand control, in Proc. 1989 Int. Conf. on Robotics and Automation, Scottsdale, AZ, USA, 1989, pp. 1758-1763.
[4]
Lin Y. and Sun Y., Grasp mapping using locality preserving projections and kNN regression, in Proc. 2013 IEEE Int. Conf. on Robotics and Automation, Karlsruhe, Germany, 2013, pp. 1076-1081.
[5]
Bócsi B., Csató L., and Peters J., Alignment-based transfer learning for robot models, in Proc. 2013 Int. Joint Conf. on Neural Networks, Dallas, TX, USA, 2013, pp. 1-7.
[6]
Zhou J. T., Tsang I. W., Pan S. J., and Tan M. K., Heterogeneous domain adaptation for multiple classes, in Proc. 17th Int. Conf. on Artificial Intelligence and Statistics, Reykjavik, Iceland, 2014, pp. 1095-1103.
[7]
Schaal S., Dynamic movement primitives—A framework for motor control in humans and humanoid robotics, in Adaptive Motion of Animals and Machines, Kimura H., Tsuchiya K., Ishiguro A., and Witte H., eds. Springer, 2006, pp. 261–280.
[8]
Pastor P., Hoffmann H., Asfour T., and Schaal S., Learning and generalization of motor skills by learning from demonstration, in Proc. 2009 IEEE Int. Conf. on Robotics and Automation, Kobe, Japan, 2009, pp. 763-768.
[9]
Ijspeert A. J., Nakanishi J., Hoffmann H., Pastor P., and Schaal S., Dynamical movement primitives: Learning attractor models for motor behaviors, Neural Comput., vol. 25, no. 2, pp. 328-373, 2013.
[10]
Metzen J. H., Fabisch A., Senger L., de G. Fernández J., and Kirchner E. A., Towards learning of generic skills for robotic manipulation, Künstl. Intell., vol. 28, no. 1, pp. 15-20, 2014.
[11]
Yu T. H., Finn C., Xie A. N., Dasari S., Zhang T. H., Abbeel P., and Levine S., One-shot imitation from observing humans via domain-adaptive meta-learning, arXiv preprint arXiv: 1802.01557, 2018.
[12]
Hussein A., Gaber M. M., Elyan E., and Jayne C., Imitation learning: A survey of learning methods, ACM Comput. Surv., vol. 50, no. 2, p. 21, 2017.
[13]
Kober J., Bagnel J., and Peters J., Reinforcement learning in robotics: A survey, Int. J. Rob. Res., vol. 32, no. 11, pp. 1238-1274, 2013.
[14]
Ijspeert A. J., Nakanishi J., and Schaal S., Movement imitation with nonlinear dynamical systems in humanoid robots, in Proc. 2002 IEEE Int. Conf. on Robotics and Automation, Washington, DC, USA, 2002, pp. 1398-1403.
[15]
Herzog S., Wörgötter F., and Kulvicius T., Optimal trajectory generation for generalization of discrete movements with boundary conditions, in Proc. 2016 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, Daejeon, Korea, 2016, pp. 3143-3149.
[16]
Fang B., Sun F. C., Liu H. P., and Guo D., Development of a wearable device for motion capturing based on magnetic and inertial measurement units, Scientific Programming, vol. 2017, p. 7594763, 2017.
[17]
Fang B., Sun F. C., Liu H. P., Guo D., Chen W. D., and Yao G. D., Robotic teleoperation systems using a wearable multimodal fusion device, Int. J. Adv. Rob. Syst., vol. 14, no. 4, pp. 1-11, 2017.
[18]
Ang K. H., Chong G., and Li Y., PID control system analysis, design, and technology, IEEE Trans. Control Syst. Technol., vol. 13, no. 4, pp. 559-576, 2005.
[19]
Vakanski A., Mantegh I., Irish A., and Janabi-Sharifi F., Trajectory learning for robot programming by demonstration using hidden markov model and dynamic time warping, IEEE Trans. Syst., Man, Cybern. B: Cybern., vol. 42, no. 4, pp. 1039-1052, 2012.
