References

1. D Adiwardana, Akihiro Matsukawa & Jay Whang (2016): Using generative models for semi-supervised learning. In: Medical image computing and computer-assisted intervention–MICCAI 2016, pp. 106–14.
2. Muneeb Imtiaz Ahmad, Jasmin Bernotat, Katrin Lohan & Friederike Eyssel (2019): Trust and Cognitive Load During Human-Robot Interaction. arXiv preprint arXiv:1909.05160.
3. Brenna D Argall, Sonia Chernova, Manuela Veloso & Brett Browning (2009): A survey of robot learning from demonstration. Robotics and autonomous systems 57(5), pp. 469–483, doi:10.1016/j.robot.2008.10.024.
4. Christopher G Atkeson & Stefan Schaal (1997): Robot learning from demonstration. In: ICML 97. Citeseer, pp. 12–20.
5. Jamshid Bagherzadeh & Hasan Asil (2019): A review of various semi-supervised learning models with a deep learning and memory approach. Iran Journal of Computer Science 2(2), pp. 65–80, doi:10.1007/s42044-018-00027-6.
6. Eric Bauer & Ron Kohavi (1999): An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine learning 36(1-2), pp. 105–139, doi:10.1023/A:1007515423169.
7. A. Billard & D. Grollman (2013): Robot learning by demonstration. Scholarpedia 8(12), pp. 3824, doi:10.4249/scholarpedia.3824. Revision #138061.
8. Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller & Jiakai Zhang (2016): End to end learning for self-driving cars. arXiv preprint arXiv:1604.07316.
9. Jürgen Börstler, Michael E Caspersen & Marie Nordström (2016): Beauty and the Beast: on the readability of object-oriented example programs. Software quality journal 24(2), pp. 231–246, doi:10.1007/s11219-015-9267-5.
10. Jürgen Börstler, Marie Nordström & James H Paterson (2011): On the quality of examples in introductory Java textbooks. ACM Transactions on Computing Education (TOCE) 11(1), pp. 1–21, doi:10.1145/1921607.1921610.
11. Michael Burke, Svetlin Penkov & Subramanian Ramamoorthy (2019): From Explanation to Synthesis: Compositional Program Induction for Learning From Demonstration. Robotics: Science and Systems (R:SS), doi:10.15607/RSS.2019.XV.015.
12. Jesse Butterfield, Sarah Osentoski, Graylin Jay & Odest Chadwicke Jenkins (2010): Learning from demonstration using a multi-valued function regressor for time-series data. In: 2010 10th IEEE-RAS International Conference on Humanoid Robots. IEEE, pp. 328–333, doi:10.1109/ICHR.2010.5686284.
13. Eduardo F Camacho & Carlos Bordons Alba (2013): Model predictive control. Springer Science & Business Media, doi:10.1007/978-1-4471-3398-8.
14. Olivier Chapelle, Bernhard Scholkopf & Alexander Zien (2009): Semi-supervised learning (chapelle, o. et al., eds.; 2006)[book reviews]. IEEE Transactions on Neural Networks 20(3), pp. 542–542, doi:10.1109/TNN.2009.2015974.
15. Silvia Chiappa & Jan R Peters (2010): Movement extraction by detecting dynamics switches and repetitions. In: Advances in neural information processing systems, pp. 388–396.
16. Antonio Criminisi, Patrick Perez & Kentaro Toyama (2003): Object removal by exemplar-based inpainting. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. 2. IEEE, pp. II–II, doi:10.1109/CVPR.2003.1211538.
17. Antonio Criminisi, Patrick Pérez & Kentaro Toyama (2004): Region filling and object removal by exemplar-based image inpainting. IEEE Transactions on image processing 13(9), pp. 1200–1212, doi:10.1109/TIP.2004.833105.
18. C. Daniel, G. Neumann & J. Peters (2012): Learning concurrent motor skills in versatile solution spaces. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 3591–3597, doi:10.1109/IROS.2012.6386047.
19. Arthur P Dempster, Nan M Laird & Donald B Rubin (1977): Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society: Series B (Methodological) 39(1), pp. 1–22.
20. Kevin R Dixon & Pradeep K Khosla (2004): Trajectory representation using sequenced linear dynamical systems. In: IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA'04. 2004 4. IEEE, pp. 3925–3930, doi:10.1109/ROBOT.2004.1308881.
21. Filip Karlo Došilovi\'c, Mario Brči\'c & Nikica Hlupi\'c (2018): Explainable artificial intelligence: A survey. In: 2018 41st International convention on information and communication technology, electronics and microelectronics (MIPRO). IEEE, pp. 0210–0215, doi:10.23919/MIPRO.2018.8400040.
22. Yiwei Fu, Devesh K Jha, Zeyu Zhang, Zhenyuan Yuan & Asok Ray (2019): Neural Network-Based Learning from Demonstration of an Autonomous Ground Robot. Machines 7(2), pp. 24, doi:10.3390/machines7020024.
23. Zoubin Ghahramani & Geoffrey E Hinton (2000): Variational learning for switching state-space models. Neural computation 12(4), pp. 831–864, doi:10.1162/089976600300015619.
24. Andrew B Goldberg & Xiaojin Zhu (2006): Seeing stars when there aren't many stars: graph-based semi-supervised learning for sentiment categorization. In: Proceedings of the first workshop on graph based methods for natural language processing. Association for Computational Linguistics, pp. 45–52, doi:10.3115/1654758.1654769.
25. Elena Gribovskaya, S. M. Khansari-Zadeh & Aude Billard (2011): Learning Nonlinear Multivariate Dynamics of Motion in Robotic Manipulators [accepted]. International Journal of Robotics Research 30(8), pp. 80–117, doi:10.1177/0278364910376251. Available at http://infoscience.epfl.ch/record/148817.
26. D. H. Grollman & O. C. Jenkins (2010): Incremental learning of subtasks from unsegmented demonstration. In: 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 261–266, doi:10.1109/IROS.2010.5650500.
27. Daniel H Grollman & Odest Chadwicke Jenkins (2008): Sparse incremental learning for interactive robot control policy estimation. In: 2008 IEEE International Conference on Robotics and Automation. IEEE, pp. 3315–3320, doi:10.1109/ROBOT.2008.4543716.
28. Helen Hastie, Katrin Lohan, Mike Chantler, David A Robb, Subramanian Ramamoorthy, Ron Petrick, Sethu Vijayakumar & David Lane (2018): The ORCA hub: Explainable offshore robotics through intelligent interfaces. arXiv preprint arXiv:1803.02100.
29. Marco Hutter, Christian Gehring, Andreas Lauber, Fabian Gunther, Carmine Dario Bellicoso, Vassilios Tsounis, Péter Fankhauser, Remo Diethelm, Samuel Bachmann & Michael Blösch (2017): ANYmal-toward legged robots for harsh environments. Advanced Robotics 31(17), pp. 918–931, doi:10.1080/01691864.2017.1378591.
30. Jinkyu Kim & John Canny (2017): Interpretable learning for self-driving cars by visualizing causal attention. In: Proceedings of the IEEE international conference on computer vision, pp. 2942–2950, doi:10.1109/ICCV.2017.320.
31. Augustine Kong, Jun S Liu & Wing Hung Wong (1994): Sequential imputations and Bayesian missing data problems. Journal of the American statistical association 89(425), pp. 278–288, doi:10.1080/01621459.1994.10476469.
32. George Konidaris & Andrew G Barto (2009): Skill discovery in continuous reinforcement learning domains using skill chaining. In: Advances in neural information processing systems, pp. 1015–1023.
33. Alex Krizhevsky, Ilya Sutskever & Geoffrey E Hinton (2012): Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp. 1097–1105, doi:10.1145/3065386.
34. Dana Kuli\'c, Christian Ott, Dongheui Lee, Junichi Ishikawa & Yoshihiko Nakamura (2012): Incremental Learning of Full Body Motion Primitives and Their Sequencing Through Human Motion Observation. Int. J. Rob. Res. 31(3), pp. 330–345, doi:10.1177/0278364911426178.
35. Brenden M Lake, Ruslan Salakhutdinov & Joshua B Tenenbaum (2015): Human-level concept learning through probabilistic program induction. Science 350(6266), pp. 1332–1338, doi:10.1126/science.aab3050.
36. Brenden M Lake, Tomer D Ullman, Joshua B Tenenbaum & Samuel J Gershman (2017): Building machines that learn and think like people. Behavioral and brain sciences 40, doi:10.1017/S0140525X16001837.
37. Sergey Levine & Pieter Abbeel (2014): Learning neural network policies with guided policy search under unknown dynamics. In: Advances in Neural Information Processing Systems, pp. 1071–1079.
38. Sergey Levine, Chelsea Finn, Trevor Darrell & Pieter Abbeel (2016): End-to-end training of deep visuomotor policies. The Journal of Machine Learning Research 17(1), pp. 1334–1373.
39. Fangtao Huang Li, Minlie Huang, Yi Yang & Xiaoyan Zhu (2011): Learning to identify review spam. In: Twenty-second international joint conference on artificial intelligence.
40. Zachary C Lipton (2018): The mythos of model interpretability. Queue 16(3), pp. 31–57, doi:10.1145/3233231.
41. Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu & Alexander C Berg (2016): Ssd: Single shot multibox detector. In: European conference on computer vision. Springer, pp. 21–37, doi:10.1007/978-3-319-46448-0_2.
42. Yin Lou, Rich Caruana & Johannes Gehrke (2012): Intelligible models for classification and regression. In: Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 150–158, doi:10.1145/2339530.2339556.
43. Laurens van der Maaten & Geoffrey Hinton (2008): Visualizing data using t-SNE. Journal of machine learning research 9(Nov), pp. 2579–2605.
44. Olivier Mangin & Pierre-Yves Oudeyer (2011): Unsupervised learning of simultaneous motor primitives through imitation. In: IEEE ICDL-EPIROB 2011, Frankfurt, Germany. Available at https://hal.archives-ouvertes.fr/hal-00652346.
45. Gary F Marcus (2018): The algebraic mind: Integrating connectionism and cognitive science. MIT press, doi:10.7551/mitpress/1187.001.0001.
46. Roderick Murray-Smith & T Johansen (1997): Multiple model approaches to nonlinear modelling and control. CRC press.
47. Andrew Y Ng & Stuart J Russell (2000): Algorithms for inverse reinforcement learning.. In: Icml 1, pp. 2.
48. Èric Pairet, Paola Ardón, Xingkun Liu, José Lopes, Helen Hastie & Katrin S Lohan (2019): A Digital Twin for Human-Robot Interaction. In: 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, pp. 372–372, doi:10.1109/HRI.2019.8673015.
49. Peter Pastor, Heiko Hoffmann, Tamim Asfour & Stefan Schaal (2009): Learning and generalization of motor skills by learning from demonstration. In: 2009 IEEE International Conference on Robotics and Automation. IEEE, pp. 763–768, doi:10.1109/ROBOT.2009.5152385.
50. Svetlin Penkov & Subramanian Ramamoorthy (2017): Using program induction to interpret transition system dynamics. arXiv preprint arXiv:1708.00376.
51. Lerrel Pinto & Abhinav Gupta (2016): Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours. In: 2016 IEEE international conference on robotics and automation (ICRA). IEEE, pp. 3406–3413, doi:10.1109/ICRA.2016.7487517.
52. Charles R Qi, Hao Su, Kaichun Mo & Leonidas J Guibas (2017): Pointnet: Deep learning on point sets for 3d classification and segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 652–660.
53. Aravind Rajeswaran, Vikash Kumar, Abhishek Gupta, Giulia Vezzani, John Schulman, Emanuel Todorov & Sergey Levine (2017): Learning complex dexterous manipulation with deep reinforcement learning and demonstrations. arXiv preprint arXiv:1709.10087, doi:10.15607/RSS.2018.XIV.049.
54. Joseph Redmon & Ali Farhadi (2018): Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
55. Shaoqing Ren, Kaiming He, Ross Girshick & Jian Sun (2015): Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in neural information processing systems, pp. 91–99, doi:10.1109/TPAMI.2016.2577031.
56. Marco Tulio Ribeiro, Sameer Singh & Carlos Guestrin (2016): " Why should I trust you?" Explaining the predictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, pp. 1135–1144, doi:10.18653/v1/N16-3020.
57. Clearpath Robotics (2014): Husky, unmanned ground vehicle. Available at https://clearpathrobotics.com/husky-unmanned-ground-vehicle-robot.
58. Wojciech Samek, Thomas Wiegand & Klaus-Robert Müller (2017): Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
59. Makoto Sato & Hiroshi Tsukimoto (2001): Rule extraction from neural networks via decision tree induction. In: IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No. 01CH37222) 3. IEEE, pp. 1870–1875, doi:10.1109/IJCNN.2001.938448.
60. Stefan Schaal (2006): Dynamic movement primitives-a framework for motor control in humans and humanoid robotics. In: Adaptive motion of animals and machines. Springer, pp. 261–280, doi:10.1007/4-431-31381-8_23.
61. Stefan Schaal, Jan Peters, Jun Nakanishi & Auke Ijspeert (2005): Learning movement primitives. In: Robotics research. the eleventh international symposium. Springer, pp. 561–572, doi:10.1007/11008941_60.
62. Ramprasaath R Selvaraju, Michael Cogswell, Abhishek Das, Ramakrishna Vedantam, Devi Parikh & Dhruv Batra (2017): Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626, doi:10.1007/s11263-019-01228-7.
63. Karen Simonyan, Andrea Vedaldi & Andrew Zisserman (2013): Deep inside convolutional networks: Visualising image classification models and saliency maps. arXiv preprint arXiv:1312.6034.
64. Jost Tobias Springenberg (2015): Unsupervised and semi-supervised learning with categorical generative adversarial networks. arXiv preprint arXiv:1511.06390.
65. John Sweller (2011): Cognitive load theory. In: Psychology of learning and motivation 55. Elsevier, pp. 37–76, doi:10.1007/978-1-4419-1428-6_446.
66. Arnaud Van Looveren & Janis Klaise (2019): Interpretable counterfactual explanations guided by prototypes. arXiv preprint arXiv:1907.02584.
67. Sandra Wachter, Brent Mittelstadt & Chris Russell (2017): Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GPDR. Harv. JL & Tech. 31, pp. 841, doi:10.2139/ssrn.3063289.
68. Ziyu Wang, Tom Schaul, Matteo Hessel, Hado Hasselt, Marc Lanctot & Nando Freitas (2016): Dueling network architectures for deep reinforcement learning. In: International conference on machine learning, pp. 1995–2003.
69. Yaochu Jin (2000): Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Transactions on Fuzzy Systems 8(2), pp. 212–221, doi:10.1109/91.842154.
70. Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel & Sergey Levine (2018): One-shot imitation from observing humans via domain-adaptive meta-learning. arXiv preprint arXiv:1802.01557, doi:10.15607/RSS.2018.XIV.002.
71. Matthew D Zeiler & Rob Fergus (2014): Visualizing and understanding convolutional networks. In: European conference on computer vision. Springer, pp. 818–833, doi:10.1007/978-3-319-10590-1_53.
72. Xiaojin Jerry Zhu (2005): Semi-supervised learning literature survey. Technical Report. University of Wisconsin-Madison Department of Computer Sciences.
73. Yuke Zhu, Ziyu Wang, Josh Merel, Andrei Rusu, Tom Erez, Serkan Cabi, Saran Tunyasuvunakool, János Kramár, Raia Hadsell & Nando de Freitas (2018): Reinforcement and imitation learning for diverse visuomotor skills. arXiv preprint arXiv:1802.09564, doi:10.15607/RSS.2018.XIV.009.
74. Jan Ruben Zilke, Eneldo Loza Mencía & Frederik Janssen (2016): Deepred–rule extraction from deep neural networks. In: International Conference on Discovery Science. Springer, pp. 457–473, doi:10.1007/978-3-319-46307-0_29.

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