Rapid Learning in Robotics - Jorg Walter Part 11 pps
Rapid Learning in Robotics - Jorg Walter Part 11 pps
... ReportSFB360-TR-9 6-3 , Universität Bielefeld, D-33615 Bielefeld. Walter, J., H. Ritter, and K. Schulten (1990, June). Non-linear predic-tion with self-organizing maps. In Int. Joint Conf. on ... SFB360-TR-9 6-4 , TF-AG-NI, Universität Bielefeld, D-33615Bielefeld. Walter, J. and H. Ritter (1996d). Rapid learning with parametrized self-organizing maps. Neurocomputing 12, 131–153. Walter, J. and ... De-partment der Technische Universität München. Walter, J. (1996). SORMA: Interoperating distributed robotics hardware. In Proc. Int. Conf. on Robotics and Automation (ICRA-97), pp. (submit-ted). Walter, ...
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Rapid Learning in Robotics - Jorg Walter Part 2 ppsx
... comply to extra constraints.Chapter 9 turns to the next higher level of one-shot learning. Here the learning of prototypical mappings is used to rapidly adapt a learning sys-tem to new context ... investment learning stage, since effort is invested, to train the system for the second,the one-shot learning phase. Observing the context, the system can nowadapt most rapidly by “mixing” the ... disciplines, and in- cludes also material, engineering, control, and communication sci-ences.The time for gathering training data becomes a major issue. Thisincludes also the time for preparing...
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Rapid Learning in Robotics - Jorg Walter Part 3 ppsx
... training examples in a stochastic sequence. Iterative learning is usually more efficient,particularly w.r.t. memory requirements.Off-line versus On-line Learning and Interferences: Off-line learning ... by the so-called “catastrophic inter-ference”, see “on-line learning below.Batch versus Incremental Learning: Calculating the network weight up-dates under consideration of all training examples ... VariablesNeural Networks Learning LearningMachine Learning Sub-symbolic & Fuzzy Learning LearningMathematics Approximation QuantizationStatistics Regression ClassificationEngineering System Identification...
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Rapid Learning in Robotics - Jorg Walter Part 10 pps
... efficient learning modules for the continuous and smooth mapping domain, the“mixture-of-expert” scheme is superior in managing mapping domainswhich require non-continuous or non-smooth interfaces. ... random lo-cations (from within the range of the training set) seen in 10 different138 “Mixture-of-Expertise” or “Investment Learning camera setups, from within the square grid of the training positions,located ... emphasizes an important point for the construction of more pow-erful learning systems: in addition to focusing on output value learning, 132 “Mixture-of-Expertise” or “Investment Learning The solution...
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Rapid Learning in Robotics - Jorg Walter Part 1 docx
... 1068.1 [a–d] Kinematic workspace of the TUM robot finger . . . . . 1088.2 [a–e] Training and testing of the finger kinematics PSOM . . 110 Jörg A. Walter Rapid Learning in Robotics Robotics deals ... efficient mapping which solves the de-sired learning task? Chapter 3 explains Kohonen's “Self-Organizing Map”procedure and techniques to improve the learning of continuous, high-dimensional ... Learning in Robotics 1Die Deutsche Bibliothek — CIP Data Walter, Jörg Rapid Learning in Robotics / by Jörg Walter, 1st ed.Göttingen: Cuvillier, 1996Zugl.: Bielefeld, Univ., Diss. 1996ISBN 3-8 958 8-7 2 8-5 Copyright:c1997,...
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Rapid Learning in Robotics - Jorg Walter Part 4 pdf
... training set, but is performingbadly on the indicated (cross-marked) position.More training data: Over-fitting can be avoided when sufficient trainingpoints are available, e.g. by learning on-line. ... cooperative learning in order to increase speed and ro-bustness of learning, studied e.g. in Walter, Martinetz, and Schulten(1991) and compared to the so-called Neural-Gas Network in Walter (1991) ... achieved by replac-ing the “winner-takes-all” rule (Eq. 3.9) with a “winner-takes-most” or “soft-max” mechanism. For example, by employing Eq. 3.6 in the index spaceof lattice coordinates. Here...
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Rapid Learning in Robotics - Jorg Walter Part 6 pot
... polynomial in a selected node sub-grid, as described.For the bi-cubic, so-called tensor-product spline is usually com-puted by row-wise spline interpolation and a column spline over the rowinterpolation ... training data. The beginning in- folding of the map, e.g.seen at the lower left corner in Fig. 5.8 demonstrates further that showsmultiple solutions (Eq. 4.4) for finding a best-match in . In ... location ( ) in the map-ping manifold . This is the source of curvature information utilized bythe PSOM to embed a smooth continuous manifold in . However, in certain cases input-output mappings are...
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Rapid Learning in Robotics - Jorg Walter Part 8 ppt
... augmenting 729 joint angle vectors on a rectangular 3 3 3 3 3 3grid in joint angle space with the missing – 112 Application Examples in the Robotics Domain -4 0 -3 0 -2 0 -1 0010203040 -4 0 -3 0 -2 0 -1 0010203090100 110 120130140150160xyzr ... Kinematics Mapping 113 -4 0 -3 0 -2 0 -1 0010203040 -4 0 -3 0 -2 0 -1 0010203090100 110 120130140150160xyzr s1 s2 A∈S wa a θ Figure 8.5: The same 27 training data vectors ... Application Examples in the Vision Domain7.3 Low Level Vision Domain: a Finger Tip Lo-cation FinderSo far, we have been investigating PSOMs for learning tasks in the contextof well pre-processed data...
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Rapid Learning in Robotics - Jorg Walter Part 9 docx
... Application Examples in the Robotics Domain2. What is the in uence of standard and Chebyshev-spaced samplingof training points inside their working interval? When the data val-ues (here 3 per ... mappings are smooth in certain domains, but non-continuous in others. Then, different types of learning experts, like PSOMs,Meta-PSOMs, LLMs, RBF and others can be chosen. The domain weight-ing ... construct thehigh-dimensional manifold from a small number of training data turns outto be here a many-sided beneficial mechanism for rapid learning. 9.2 “Investment Learning or “Mixture-of-Expertise”...
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rapid learning in robotics jorg walter pot
... training examples in a stochastic sequence. Iterative learning is usually more efficient,particularly w.r.t. memory requirements.Off-line versus On-line Learning and Interferences: Off-line learning ... by the so-called “catastrophic inter-ference”, see “on-line learning below.Batch versus Incremental Learning: Calculating the network weight up-dates under consideration of all training examples ... cooperative learning in order to increase speed and ro-bustness of learning, studied e.g. in Walter, Martinetz, and Schulten(1991) and compared to the so-called Neural-Gas Network in Walter (1991)...
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