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Autonomous Motion Conference Paper A kendama learning robot based on a dynamic optimiation principle Miyamoto, H., Gandolfo, F., Gomi, H., Schaal, S., Koike, Y., Rieka, O., Nakano, E., Wada, Y., Kawato, M. In Preceedings of the International Conference on Neural Information Processing, 938-942, Hong Kong, September 1996, clmc BibTeX
Empirical Inference Conference Paper Incorporating invariances in support vector learning machines Schölkopf, B., Burges, C., Vapnik, V. In Artificial Neural Networks --- ICANN‘96, Artificial Neural Networks: ICANN 96, LNCS vol. 1112, 47-52, (Editors: C von der Malsburg and W von Seelen and JC Vorbrüggen and B Sendhoff), Springer, Berlin, Germany, 6th International Conference on Artificial Neural Networks, July 1996, volume 1112 of Lecture Notes in Computer Science
Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding knowledge about invariances of a classification problem at hand. We present a method of incorporating prior knowledge about transformation invariances by applying transformations to support vectors, the training examples most critical for determining the classification boundary.
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Autonomous Motion Article A Kendama learning robot based on bi-directional theory Miyamoto, H., Schaal, S., Gandolfo, F., Koike, Y., Osu, R., Nakano, E., Wada, Y., Kawato, M. Neural Networks, 9(8):1281-1302, 1996, clmc
A general theory of movement-pattern perception based on bi-directional theory for sensory-motor integration can be used for motion capture and learning by watching in robotics. We demonstrate our methods using the game of Kendama, executed by the SARCOS Dextrous Slave Arm, which has a very similar kinematic structure to the human arm. Three ingredients have to be integrated for the successful execution of this task. The ingredients are (1) to extract via-points from a human movement trajectory using a forward-inverse relaxation model, (2) to treat via-points as a control variable while reconstructing the desired trajectory from all the via-points, and (3) to modify the via-points for successful execution. In order to test the validity of the via-point representation, we utilized a numerical model of the SARCOS arm, and examined the behavior of the system under several conditions.
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Autonomous Motion Book Chapter From isolation to cooperation: An alternative of a system of experts Schaal, S., Atkeson, C. G. In Advances in Neural Information Processing Systems 8, 605-611, (Editors: Touretzky, D. S.;Mozer, M. C.;Hasselmo, M. E.), MIT Press, Cambridge, MA, 1996, clmc
We introduce a constructive, incremental learning system for regression problems that models data by means of locally linear experts. In contrast to other approaches, the experts are trained independently and do not compete for data during learning. Only when a prediction for a query is required do the experts cooperate by blending their individual predictions. Each expert is trained by minimizing a penalized local cross validation error using second order methods. In this way, an expert is able to adjust the size and shape of the receptive field in which its predictions are valid, and also to adjust its bias on the importance of individual input dimensions. The size and shape adjustment corresponds to finding a local distance metric, while the bias adjustment accomplishes local dimensionality reduction. We derive asymptotic results for our method. In a variety of simulations we demonstrate the properties of the algorithm with respect to interference, learning speed, prediction accuracy, feature detection, and task oriented incremental learning. 
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Materials Article Influence of humidity on polycrystalline Cu(In,Ga)Se-2 thin films for solar cells: A study of Na and H2O coadsorption Heske, C., Richter, G., Chen, Z. H., Fink, R., Umbach, E., Riedl, W., Karg, F. Conference Record of the Twenty Fifth Ieee Photovoltaic Specialists Conference - 1996, 861-864, 1996 DOI BibTeX
Autonomous Motion Article One-handed juggling: A dynamical approach to a rhythmic movement task Schaal, S., Sternad, D., Atkeson, C. G. Journal of Motor Behavior, 28(2):165-183, 1996, clmc
The skill of rhythmic juggling a ball on a racket is investigated from the viewpoint of nonlinear dynamics. The difference equations that model the dynamical system are analyzed by means of local and non-local stability analyses. These analyses yield that the task dynamics offer an economical juggling pattern which is stable even for open-loop actuator motion. For this pattern, two types of pre dictions are extracted: (i) Stable periodic bouncing is sufficiently characterized by a negative acceleration of the racket at the moment of impact with the ball; (ii) A nonlinear scaling relation maps different juggling trajectories onto one topologically equivalent dynamical system. The relevance of these results for the human control of action was evaluated in an experiment where subjects performed a comparable task of juggling a ball on a paddle. Task manipulations involved different juggling heights and gravity conditions of the ball. The predictions were confirmed: (i) For stable rhythmic performance the paddle's acceleration at impact is negative and fluctuations of the impact acceleration follow predictions from global stability analysis; (ii) For each subject, the realizations of juggling for the different experimental conditions are related by the scaling relation. These results allow the conclusion that for the given task, humans reliably exploit the stable solutions inherent to the dynamics of the task and do not overrule these dynamics by other control mechanisms. The dynamical scaling serves as an efficient principle to generate different movement realizations from only a few parameter changes and is discussed as a dynamical formalization of the principle of motor equivalence.
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