Abelson, R. 1963. Hot cognition. Computer Simulation of Personality.

Accame, M. and Natale, F. D. 1995. Neural tuned edge extraction in visual sensing. In Third European Workshop on Learning Robots, Heraklion, Kreta.

Agre, P. and Chapman, D. 1990. What are plans for? In Maes, P., editor, Designing Autonomous Agents, pages 17 34. MIT Press.

Andreae, P. M. 1984. Constraint limited generalization: Aquiring procedures from examples. In Proceedings of the National Conference on Arti cial Intelligence AAAI, pages 6 10.

Anzai, Y. 1993. Human-computer-interaction in multianget environments. In Procs. of HCI International'93, pages 2 7, New York. Elsevier.

Asada, H. and Liu, S. 1991. Transfer of human skills to neural net robot controllers. In IEEE International Conference on Robotics and Automation, pages 2442 2448, Sacramento. Asoh, H., Motomura, Y., Hara, I., Akaho, S., Hayamizu, S., and Matsui,

T. 1996. Aquiring a probalistic map with dialogue-based learning. In Hexmoor, H. and Meeden, L., editors, ROBOLEARN '96: An Intern. Workshop on Learning for Autonomous Robots, pages 11 18.

Billard, A. 1996. Learning to speak through imitation for social robots. Master's thesis, University of Edinburgh, Scotland, UK.

Birk, A. 1996. Learning to survive. In Klingspor, V., editor, Procs. of the 5th European Workshop on Learning Robots, pages 1 8, Bari, Italy.

Bratko, I., Urbancic, T., and Sammut, C. 1995. Behavioural cloning: Phenomena, results, and problems. In 5th IFAC Symposium on Automated Systems based on Human Skill, Berlin.

Card, S. K., Moran, T. P., and Newell, A. 1983. The psychology of humancomputer interaction. Lawrence Erlbaum Associates, Hillsdale, New Jersey.

Cypher, A. I. 1993. Watch what I do Programming by Demonstration. MIT Press, Cambridge, Massachusetts.

De Jong, G. and Mooney, R. 1986. Explanation-based-learning: An alternative view. Machine Learning, 21:145 176.

Delson, N. and West, H. 1994. The use of human inconsistency in improving 3D robot trajectories. In IEEE RSJ International Conference on Intelligent Robots and Systems, pages 1248 1255, Munchen.

Demiris, J. and Hayes, G. 1996. Imitative learning in robots and humans: Mechanisms and architectures. In Klingspor, V., editor, Fifth European Workshop on Learning Robots, Bari. Italy.

Dillmann, R., Kaiser, M., Klingspor, V., Morik, K., and Wallner, F. 1995. Teaching and understanding intelligent service robots: A Machine Learning Approach. In 19. Deutsche Jahrestagung fur Kunstliche Intelligenz KI '95 Workshop: Informationsverarbeitung in Servicerobotern, Bielefeld, Germany. Springer Verlag.

Dzeroski, S., Todorovski, L., and Urbancic, T. 1995. Handling real numbers in ILP: A step towards better behavioural clones. In European Conference on Machine Learning, pages 283 286, Heraklion, Kreta.

Forster, S., Lobing, H., and Peters, K. 1995. Architekturkonzeption eines teilautonomen Montageroboters. Report Situierte Kunstliche Kommunikatoren 95 10, Universitat Bielefeld, Sonderforschungsbereich 360.

Friedrich, H., Munch, S., Dillmann, R., Bocionek, S., and Sassin, M. 1996. Robot programming by demonstration: Supporting the induction by human interaction. Machine Learning, 23:163 189.

Giordana, A. and Saitta, L. 1990. Abstraction a general framework for learning. In Procs. AAAI Workshop on Automatic Generation of Approximations and Abstractions, pages 245 256.

Goebel, M. 1996. Lernen intervallbezogener Merkmale eines mobilen Roboters. Master's thesis, Universitat Dortmund.

Groover, M. 1986. Industrial Robotics Technology, Programming and Applications. McGraw-Hill.

Guez, A. and Selinsky, J. 1988. A neuromorphic controller with a human teacher. In IEEE International Conference on Neural Networks, pages 595 602, San Diego.

Harnad, S. 1990. The symbol grounding problem. Physica D, 42:335 346.

Hayes, G. and Demiris, J. 1994. A robot controller using learning by imitation. In International Symposium on Intelligent Robotic Systems, pages 198 204, Grenoble.

Heise, R. 1989. Demonstration instead of programming: Focussing attention in robot task acquisition. Research report 89 360 22, Department of Computer Science, University of Calgary.

Heyes, C. 1993. Imitation, culture and cognition. Animal Behaviour, 46:999 1010.

Hiraki, K. and Anzai, Y. 1996. Sharing knowledge with robots. Int. Journal of Human-Computer Interaction, 83:325 342.

Kaiser, M., Deck, M., and Dillmann, R. 1996. Acquisition of basic mobility skills from human demonstrations. Studies in Informatics and Control.

Kaiser, M. and Dillmann, R. 1996. Building elementary robot skills from human demonstration. In IEEE International Conference on Robotics and Automation, Minneapolis, Minnesota, USA.

Kaiser, M., Friedrich, H., and Dillmann, R. 1995. Obtaining good performance from a bad teacher. In International Conference on Machine Learning, Workshop on Programming by Demonstration, Tahoe City, California.

Klingspor, V., Morik, K., and Rieger, A. 1996. Learning concepts from sensor data of a mobile robot. Machine Learning Journal, 232 3:305 332.

Kuniyoshi, Y., Inaba, M., and Inoue, H. 1994. Learning by watching: Extracting reusable task knowledge from visual observation of human performance. IEEE Transactions on Robotics and Automation, 106:7998 22.

Lane, N. E. 1988. Skill Acquisition rates and patterns issues and training implications. Recent research in Psychology. Springer Verlag, Berlin, Heidelberg, New York.

Langle, T., Lueth, T., Herzog, G., Stopp, E., and Kamstrup, G. 1995. KANTRA a natural language interface for intelligent robots. In Rembold, U. and Dillmann, R., editors, Intelligent Autonomous Systems IAS-4, pages 365 372. IOS Press.

Lieberman, H. 1993. MONDRIAN: A teachable graphical editor. In Cypher, A. I., editor, Watch what I do. MIT Press, Cambridge, Massachusetts.

Lopes, L. S. and Camarinha-Matos, L. 1995a. Planning, training and learning in supervision of exible assembly systems. In Procs. of Conf. on Balanced Automation Systems.

Lopes, L. S. and Camarinha-Matos, L. M. 1995b. Inductive generation of diagnostic knowledge for autonomous assembly. In IEEE International Conference on Robotics and Automation, Nagoya, Japan.

Maulsby, D. 1994. Instructible agents. PhD thesis, University of Calgary, Canada.

McCarthy, J. and Hayes, P. J. 1969. Some philosophical problems from the standpoint of arti cial intelligence. Machine Intelligence, 5:463 502.

Meltzo , A. and Moore, M. 1989. Imitation in newborn infants: Exploring the range of gestures imitated and the underlying mechanisms. Developmental Psychology, 256:954 962.

Minton, S. 1995. Quantitative results concerning the utility of explanation based learning. In Ram, A. and Leake, D. B., editors, Goal-driven learning, pages 55 82. A Bradford Book, The MIT Press, Cambridge Massachusetts, London England.

Nadel, J. and Fontaine, A. M. 1989. Communicating by imitation: a developmental and comparative approach to transitory social competence. In Schneider, B., Attilli, G., Nadel, J., and Weissberg, R., editors, Social Competence in Developmental Perspective. Kluwer Academic Publishers.

Nelson, K. 1983. The derivation of concepts and categories from event representations. In Scholnick, E. K., editor, New Trends in Conceptual Representation: Challenges to Piaget's Theory?, chapter 6, pages 1291 49. Lawrence Erlbaum Associates, Hillsdale, NJ.

Pfeifer, R. and Scheier, C. 1994. From perception to action: The right direction? In Gaussier, P. and Nicoud, J.-D., editors, From Peception to Action, pages 1 11, Los Alamitos, CA. IEEE Computer Society Press.

Pomerleau, D. A. 1991. E cient training of arti cial neural networks for autonomous navigation. Neural Computation, 15:88 97.

Rauber, T. W., Barata, M. M., and Steiger-Garc~ao, A. S. 1993. A toolbox for analysis and visualization of sensor data in supervision. In Proc. of the Int. Conf. on Fault Diagnosis, Toulouse, France.

Reignier, P., Hansen, V., and Crowley, J. 1995. Incremental supervised learning for mobile robot reactive control. In Intelligent Autonomous Systems 4, pages 287 294, Karlsruhe.

Romann, J. 1995. Virtual reality as a control and supervision tool for autonomous systems. In Rembold, U., Dillmann, R., Hertzberger, L., and Kanade, T., editors, IAS 4, Proc. of the 4th Intern. Conference on Intelligent Autonomous Systems, pages 344 351. IOS Press.

Sa otti, A., Konolige, K., and Ruspini, E. 1995. A multivalued logic approach to integrating planning and control. Arti cial Intelligence, 761 2:481 526.

Sammut, C., Hurst, S., Kedzier, D., and Michie, D. 1992. Learning to y. In Procs. of the 9th Intern. Conf. on Machine Learning, pages 385 393.

Schraft, R. D. 1994. Serviceroboter ein Beitrag zur Innovation im Dienstleistungswesen. Fraunhofer -Institut fur Produktionstechnik und Automatisierung IPA.

Segre, A. M. 1989. Machine Learning of Robot Assembly Plans. Kluwer Academic Publishers.

Stanley, W., Mathews, R. C., Buss, R. R., and Kotler-Cope, S. 1989. Insight without awareness: on the interaction of verbalization, instruction and practice in a simulated process control task. The Quarterly Journal of Experimental Psychology, 41A3:553 577.

Steels, L. 1994. The arti cial life roots of arti cial intelligence. Arti cial Life, 11.

Stewart, J. 1995. The implications for understanding high-level cognition of a grounding in elementary adaptive systems. Robotics and Autonomous Systems, 16:107 116.

Ude, A. 1993. Trajectorygeneration from noisy positions of object features for teaching robot paths. Robotics and Autonomous Systems, 11:1131 27.

Urbancic, T. and Bratko, I. 1994. Reconstructing human skill with machine learning. In European Conference on Arti cial Intelligence, pages 498 502, Amsterdam.

Van Lehn, K. 1996. Cognitive skill acquisition. Annual Review of Psychology, 47:513 539.

Wrobel, S. 1991. Towards a model of grounded concept formation. In Proc. 12th International Joint Conference on Arti cial Intelligence, pages 712 719, Los Altos, CA. Morgan Kaufman. Learning Task Method Coverage Correctness Sensor features MLJ 28 53 MG 73 33 GRDT 73 45 Sensor group features MLJ 85 95 MG 92 61 GRDT 87 98 Perceptual features MLJ 70 70 MG 49 76 GRDT 97 100 action skill. and re nement. Gray arrows indicate feedback loops. Only some phases are permitted to require user interaction. Force skill. The commanded motions of the robot are given in 1=100 mm , the forces are measured in N . Fz in N . N . Force is shown in N . Machine Learning Programming Task specification Performance evaluation Feedback related to task execution, available skills, and perceptions Specification of skill or program Approximate Knowledge Exact Knowledge Examples Evaluation Function Prototype of robot program/trajectory/skill Initial Design Implementation Application and Refinement Interface Plan-Scheduler Synchronization Data Management Learning Component Learning Performance Object Identification Plan Refinement & Execution Robot Planner IIIPlanner I Planner II Robot state x(t) Error criterion es es = 0 ts = 0 Error in skill execution Skill successfully applied x(t) x(t) ts = 1es = 1 Evaluation function rs x(t)Termination criterion ts u(t) u(t)Control function cs r(t) x(t) Off-line learning process Example generation Example preprocessing Knowledge/Skill application Symbolic Interpretation Parameter initialization Knowledge/skill evaluation Knowledge/Skill refinement/enhancement Example segmentation Training data generation Quality assessment Identification of learning/acquisition task Determination of system characteristics -20 -15 -10 -5 0 50 100 150 200 250 300 Demonstration of Force Control (Fz = -10 lb) Fx Fy Fz dz * 100 -10 -8 -6 -4 -2 0 500 1000 1500 2000 2500 3000 3500 Force Fz Target Force -12 -10 -8 -6 -4 -2 0 1000 2000 3000 4000 5000 6000 Force Fz Target -12 -10 -8 -6 -4 -2 0 1000 2000 3000 4000 5000 6000 Force Fz -12 -10 -8 -6 -4 -2 0 2000 4000 6000 8000 10000 12000 14000 16000 Force Fz