2.2. Bases teóricas
2.2.7. Teorías de la personalidad
[1] C. Ray, F. Mondada, and R. Siegwart, “What do people expect from robots?,” inInter- national Conference on Intelligent RObots and Systems - IROS, pp. 3816–3821, 2008.
[2] B. Gates, “A Robot in Every Home,”Scientific American, vol. 296, pp. 58–65, 2007.
[3] B. D. Argall, S. Chernova, M. Veloso, and B. Browning, “A survey of robot learning from demonstration,” Robotics and Autonomous Systems, vol. 57, no. 5, pp. 469 – 483, 2009.
[4] F. Saitoh, M. Tsuboyama, F. Shen, and O. Hasegawa, “Developmental cognitive map learning and behavior acquisition by a mobile robot through a self-organizing incremental neural network,” 2009.
[5] C. DEste and C. Sammut, “Learning and generalising semantic knowledge from object scenes,” Robotics and Autonomous Systems, vol. 56, no. 11, pp. 891 – 900, 2008.
[6] J. J. Steil, F. Röthling, R. Haschke, and H. Ritter, “Situated robot learning for multi- modal instruction and imitation of grasping,”Robotics and Autonomous Systems, vol. 47, pp. 129–141, 2004.
[7] R. Jäkel, S. R. Schmidt-Rohr, M. Lösch, A. Kasper, and R. Dillmann, “Learning of generalized manipulation strategies in the context of Programming by Demonstration,” inIEEE-RAS International Conference on Humanoid Robots, 2010.
[8] R. Jäkel, S. Schmidt-Rohr, S. Rühl, A. Kasper, Z. Xue, and R. Dillmann, “Learning of planning models for dexterous manipulation based on human demonstrations,”Interna- tional Journal of Social Robotics, pp. 1–12, 2012. 10.1007/s12369-012-0162-y.
[9] M. Schneider and W. Ertel, “Robot Learning by Demonstration with local Gaussian process regression,” in International Conference on Intelligent RObots and Systems - IROS, pp. 255–260, 2010.
[10] G. Ye and R. Alterovitz, “Demonstration-Guided Motion Planning,” in International Symposium on Robotics Research (ISRR), 2011.
78 Bibliography
[11] L. Rozo, S. Calinon, D. Caldwell, P. Jiménez, and C. Torras, “Learning collaborative impedance-based robot behaviors,” parameters, vol. 1, no. 1, p. 1, 2013.
[12] V. Kruger, D. Kragic, A. Ude, and C. Geib, “The Meaning of Action A review on action recognition and mapping,” Advanced Robotics, 2007.
[13] M. Pardowitz, S. Knoop, R. Dillmann, and R. D. Zollner, “Incremental learning of tasks from user demonstrations, past experiences, and vocal comments,” Trans. Sys. Man Cyber. Part B, vol. 37, pp. 322–332, Apr. 2007.
[14] Z. Kalal, K. Mikolajczyk, and J. Matas, “Tracking-learning-detection,”Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 34, no. 7, pp. 1409–1422, 2012.
[15] R. B. Rusu and S. Cousins, “3D is here: Point Cloud Library (PCL),” in IEEE Inter- national Conference on Robotics and Automation (ICRA), (Shanghai, China), May 9-13 2011.
[16] V. Kruger, D. Herzog, S. Baby, A. Ude, and D. Kragic, “Learning Actions from Obser- vations,”IEEE Robotics & Automation Magazine, vol. 17, pp. 30–43, 2010.
[17] H. Friedrich, R. Dillmann, and O. Rogalla, “Interactive robot programming based on human demonstration and advice,” in Sensor Based Intelligent Robots (H. Christensen, H. Bunke, and H. Noltemeier, eds.), vol. 1724 of Lecture Notes in Computer Science, pp. 96–119, Springer Berlin / Heidelberg, 1999.
[18] S. Wu and Y. F. Li, “Flexible signature descriptions for adaptive motion trajectory representation, perception and recognition,” Pattern Recogn., vol. 42, pp. 194–214, Jan. 2009.
[19] M. Rudinac and P. P. Jonker, “A fast and robust descriptor for multiple-view object recognition,” in International Conference on Control, Automation, Robotics and Vision, pp. 2166–2171, 2010.
[20] X. He, T. Ogura, A. Satou, and O. Hasegawa, “Developmental Word Acquisition and Grammar Learning by Humanoid Robots Through a Self-Organizing Incremental Neural Network,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 37, pp. 1357–1372, 2007.
[21] N. Makibuchi, F. Shen, and O. Hasegawa, Online Knowledge Acquisition and General Problem Solving in a Real World by Humanoid Robots. 2010.
[22] A. Kawewong, S. Tangruamsub, P. Kankuekul, and O. Hasegawa, “Fast online incre- mental transfer learning for unseen object classification using self-organizing incremental neural networks,” inInternational Symposium on Neural Networks, pp. 749–756, 2011.
[23] H. Kjellström, J. Romero, and D. Kragic, “Visual object-action recognition: Inferring object affordances from human demonstration,” Computer Vision and Image Under- standing, vol. 115, pp. 81–90, 2011.
[24] M. Madry, D. Song, and D. Kragic, “From object categories to grasp transfer using probabilistic reasoning,” in Robotics and Automation (ICRA), 2012 IEEE International Conference on, pp. 1716 –1723, may 2012.
79
[25] H. Grabner, J. Gall, and L. V. Gool, “What makes a chair a chair?,” inComputer Vision and Pattern Recognition, pp. 1529–1536, 2011.
[26] H. Friedrich and R. Dillmann, “Robot Programming Based On A Single Demonstration And User Intentions,” inEuropean Workshop on Learning Robots, 1995.
[27] H. Friedrich, S. Mnch, R. Dillmann, S. Bocionek, and M. Sassin, “Robot programming by demonstration (rpd): Supporting the induction by human interaction,”Machine Learn- ing, vol. 23, pp. 163–189, 1996. 10.1023/A:1018224721118.
[28] M. N. Nicolescu and M. J. Mataric, “Natural methods for robot task learning: instruc- tive demonstrations, generalization and practice,” in Autonomous Agents & Multiagent Systems/Agent Theories, Architectures, and Languages, pp. 241–248, 2003.
[29] P. E. Rybski, K. Yoon, J. Stolarz, and M. M. Veloso, “Interactive robot task training through dialog and demonstration,” inProceedings of the ACM/IEEE international con- ference on Human-robot interaction, HRI ’07, (New York, NY, USA), pp. 49–56, ACM, 2007.
[30] C. Fritz and Y. Gil, “A formal framework for combining natural instruction and demon- stration for end-user programming,” inIntelligent User Interfaces, pp. 237–246, 2011. [31] H. Holzapfel, D. Neubig, and A. Waibel, “A dialogue approach to learning object de-
scriptions and semantic categories,” Robotics and Autonomous Systems, vol. 56, no. 11, pp. 1004 – 1013, 2008.
[32] A. L. Thomaz and C. Breazeal, “Teachable robots: Understanding human teaching be- havior to build more effective robot learners,” Artificial Intelligence, vol. 172, pp. 716– 737, 2008.
[33] B. D. Argall, B. Browning, and M. M. Veloso, “Learning robot motion control with demonstration and advice-operators,” in International Conference on Intelligent Robots and Systems, pp. 399–404, 2008.
[34] B. D. Argall, B. Browning, and M. M. Veloso, “Automatic weight learning for multiple data sources when learning from demonstration,” inInternational Conference on Robotics and Automation, pp. 226–231, 2009.
[35] R. Maclin and J. W. Shavlik, “Creating Advice-Taking Reinforcement Learners,” Ma- chine Learning, vol. 22, pp. 251–281, 1996.
[36] G. Kuhlmann, P. Stone, R. Mooney, and J. Shavlik, “Guiding a Reinforcement Learner with Natural Language Advice: Initial Results in RoboCup Soccer,” 2004.
[37] S. Ekvall and D. Kragic, “Robot Learning from Demonstration: A Task-Level Planning Approach,” 2008.
[38] G. Konidaris, S. Kuindersma, R. Grupen, and A. Barto, “Robot learning from demon- stration by constructing skill trees,”Int. J. Rob. Res., vol. 31, pp. 360–375, Mar. 2012. [39] R. Jakel, P. Meissner, S. R. Schmidt-Rohr, and R. Dillmann, “Distributed generalization
of learned planning models in robot Programming by Demonstration,” in International Conference on Intelligent RObots and Systems - IROS, pp. 4633–4638, 2011.
80 Bibliography
[40] S. R. Buss, “Introduction to inverse kinematics with jacobian transpose, pseudoinverse and damped least squares methods,”
[41] P. Sanguansat, “Multiple multidimensional sequence alignment using generalized dy- namic time warping,”WSEAS TRANSACTIONS on MATHEMATICS, vol. 11, pp. 668– 678, 2012.
[42] C. Audet and J. E. Dennis Jr, “Analysis of generalized pattern searches,”SIAM Journal on Optimization, vol. 13, no. 3, pp. 889–903, 2002.
[43] F. L. Markley, Y. Cheng, J. L. Crassidis, and Y. Oshman, “Averaging quaternions,” Journal of Guidance, Control, and Dynamics, vol. 30, no. 4, pp. 1193–1197, 2007.
[44] R. H. Byrd, J. C. Gilbert, and J. Nocedal, “A trust region method based on interior point techniques for nonlinear programming,”Mathematical Programming, vol. 89, no. 1, pp. 149–185, 2000.
[45] C. Meijneke, G. Kragten, and M. Wisse, “Design and performance assessment of an underactuated hand for industrial applications,”Mech Sci, vol. 2, pp. 9–15, 2011.
[46] J. Weng, J. Mcclelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, and E. The- len, “ARTIFICIAL INTELLIGENCE: Autonomous Mental Development by Robots and Animals,” Science, vol. 291, pp. 599–600, 2001.
[47] M. Pardowitz and R. Dillmann, “Towards life-long learning in household robots: The Piagetian approach,” in IEEE International Conference on Development and Learning, 2007.
[48] G. Guerra-Filho and Y. Aloimonos, “The syntax of human actions and interactions,” Journal of Neurolinguistics, vol. 25, no. 5, pp. 500 – 514, 2012.
[49] B. Moldovan, M. van Otterlo, P. Moreno, J. Santos-Victor, and L. D. Raedt, “Statistical relational learning of object affordances for robotic manipulation,” Latest advances in inductive logic programming, 2012.
[50] M. E. Taylor, H. B. Suay, and S. Chernova, “Integrating reinforcement learning with human demonstrations of varying ability,” in The 10th International Conference on Au- tonomous Agents and Multiagent Systems - Volume 2, AAMAS ’11, (Richland, SC), pp. 617–624, International Foundation for Autonomous Agents and Multiagent Systems, 2011.
[51] C. G. Atkeson and S. Schaal, “Robot Learning From Demonstration,” in International Conference on Machine Learning, pp. 12–20, 1997.