Abstract
Programming by Demonstration (PbD) is a programming method that allows to add new functionalities to a system by simply showing the desired task or skill in form of few examples. In the domain of robotics this paradigm offers the potential to reduce the complexity of robot task programming and to make programming more ”natural”. In case of programming an assembly task PbD allows with the help of a video or a laser camera and a data glove the automatic generation the necessary robot program for the assembly task. In addition, the demonstration of the task with few different assembly situations and strategies may achieve a generalized assembly function for all possible variants of the class. In order to realize such a PbD system at least two major problems have to be solved. First, the sensor data trace of a demonstration has to be interpreted and transformed into a high-level situation-action representation. This task is not yet well understood nor solved in general. Second, if a generalization is required, induction algorithms must be applied to the sensor data trace, to find the most general user-intended robot function from only few examples. In this paper mainly the second problem is focused. The described experimental PbD environment consists of an industrial robot, a 6D space mouse used as input device, and some sensors. Various data can be recorded during a demonstration for further processing in the PbD system implemented on a workstation. The objective is to exploit the possibilities of integrating learning and clustering algorithms for automated robot programming. In particular it is investigated how human interaction with the PbD system as well as user-initiated dialogs can support inductive learning to acquire generalized assembly programs and skills.
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References
Andrea, P.M., Justified Generalization: Acquiring Procedures from Examples, Technical Report AI-TR-834. Artificial Intelligence Laboratory, MIT, 1985.
Atkeson, C.G., Aboaf, E.W., McIntyre, J., and Reinkensmeyer, D.J., Model-based robot learning, in Proceedings of the 4th International Symposium on Robotics Research, 1987.
Baroglio, C., Giordana, A., Kaiser, M., Nuttin, M., and Piola, R., Learning Controllers for Industrial Robots, Machine Learning, 1996.
Bocionek, S., Agent systems that negotiate and learn, International Journal Human-Computer Studies, 42, pp. 265–288, 1995.
Bocionek, S. and Sassin, M., Dialog-Based Learning (DBL) for Adaptive Interface Agents and Programming-by-Demonstration Systems, Technical Report CMU-CS-93-175. Carnegie Mellon University, Pittsburgh, 1993.
Cypher, A., EAGER: Programming Repetitive Tasks by Example, in CHI'91 Conference Proceedings (pp. 33–39). New Orleans, Louisiana: ACM Press, 1991.
Dufay, B. and Latombe, J.-C., An approach to automatic robot programming based on inductive learning, International Journal of Robotics Research, 3, pp. 97–115, 1984.
Fikes, R. E. and Nilsson, N. J., Strips: A new approach to the application of theorem proving to problem solving, Artificial Intelligence, 2, pp. 189–208, 1971.
Flaig, T., Neugebauer, J.-G., and Wapler, M., VR 4RobotS: a New Off-line Programming System Based on Virtual Reality Techniques, in Proceedings of the 25th International Symposium on Industrial Robots (pp. 671–678). Hannover, Germany, 1994.
Friedrich, H. and Kaiser, M., What can Robots learn from Humans?, in IFAC Workshop on Human-Oriented Design of Advanced Robotic Systems (pp. 1–6). Vienna, Austria, 1995.
Heise, R., Demonstration Instead of Programming: Focussing Attention in Robot Task Acquisition, Research Report 89/360/22. University of Calgary, 1989.
Heise, R., Programming Robots by Example, Research Report 92/476/14. University of Calgary, 1992.
Ikeuchi, K., Kawade, M., and Suehiro, T., Towards Assembly Plan from Observation: Task Recognition with Planar, Curved and Mechanical Contacts, in Proceedings of the IEEE/RJS International Conference on Intelligent Robots and Systems (pp. 2294–2301). Yokohama, Japan, 1993.
Kaiser, M., Giordana, A., and Nuttin, M., Integrated Acquisition, Execution, Evaluation and Tuning of Elementary Skills for Intelligent Robots, in Proceedings of the IFAC Symposium on Artificial Intelligence in Real Time Control (pp. 145–150). Valencia, Spain, 1994.
Kaiser, M., Retey, A., and Dillmann, R. (1995), Robot skill acquisition via human demonstration, in Proceedings of the International Conference on Advanced Robotics (pp. 763–768), Barcelona, Spain, 1995.
Kreuziger, J. and Hauser, M., A New System Architecture for Applying Symbolic Learning Techniques to Robot Manipulation, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. Yokohama, Japan, 1993.
Kuniyoshi, Y., Masayuki, I., and Inoue, H., Learning by watching: Reusable task knowledge from visual observation of human performance. IEEE Transactions on Robotics and Automation, 10, pp. 799–822, 1994.
Liu, S. and Asada, H., Teaching and training of deburring robots using neural networks, in IEEE International Conference on Robotics and Automation (pp. 339–345), 1993.
Lozano-Perez, T., Robot Programming, in Proceedings of the IEEE, 71, (pp. 821–841), 1983.
Maulsby, D.L. and Witten, I.H., Metamouse: An Instructable Agent for Programming by Demonstration, in A. Cypher (ed.), Watch What I Do: Programming by Demonstration. MIT Press, 1993.
McKerrow, P.J., Introduction to Robotics, in Electronic Systems Engineering. Addison-Wesley, 1991.
Milne, R., Building Successful Applications: The Wrong Way and the Right Way, in G. Barth et al. (eds.), KI-94 — Anwendungen der Künstlichen Intelligenz. Springer, 1991.
Münch, S., Kreuziger, J., Kaiser, M., and Dillmann, R., Robot Programming by Demonstration (RPD) — Using Machine Learning and User Interaction Methods for the Development of Easy and Comfortable Robot Programming Systems, in Proceedings of the 25th International Symposium on Industrial Robots (pp. 685–693). Hannover, Germany, 1994.
Münch, S., Sassin, M., and Bocionek, S., The Application of PbD Methods to Real-World Domains: Two Case Studies, in Proceedings of the 7th Australian Joint Conference on Artificial Intelligence (pp. 92–99). Armidale, Australia, 1994.
Neubauer, W., Bocionek, S., Möller, M., and Rencken, W., Learning Systems Behavior for the Automatic Correction and Optimization of Off-line Robot Programs, in Proceedings of the IEEE International Conference on Intelligent Robots and Systems, Raleigh, 1992.
Sassin, M., Creating user-intended programs with programming by demonstration, in Proceedings of the IEEE/CS Symposium on Visual Languages (pp. 153–160). St. Louis, Missouri, 1994.
Segre, A.M., Machine Learning of Robot Assembly Plans. Kluwer Academic Publishers, 1988.
Thrun, S.B. and Mitchell, T.M., Integrating Inductive Neural Network Learning and Explanation-based Learning, in Proceedings of the 13th International Joint Conference on AI (pp. 930–936). Chambery, France, 1993.
Ude, A., Trajectory Generation from Noisy Positions of Object Features for Teaching Robot Paths. Robotics and Autonomous Systems, 11, pp. 113–127, 1993.
Ude, A., Bröde, H., and Dillmann, R., Object Localization Using Perceptual Organization and Structural Stereopsis, in Proceedings of the 3rd International Conference on Automation, Robotics and Computer Vision, Singapore, 1994.
Waibel, A., Hanazawa, T., Hinton, G., Shikano, K., and Lang, K., Phoneme recognition using time-delay neural networks. IEEE Transactions on acoustics, speech and signal processing, pp. 328–339, 1989.
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Dillmann, R., Friedrich, H. (1996). Programming by demonstration: A machine learning approach to support skill acquision for robots. In: Calmet, J., Campbell, J.A., Pfalzgraf, J. (eds) Artificial Intelligence and Symbolic Mathematical Computation. AISMC 1996. Lecture Notes in Computer Science, vol 1138. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61732-9_52
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DOI: https://doi.org/10.1007/3-540-61732-9_52
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