Rapid Learning in Robotics - Jorg Walter Part 9 docx
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 ... engaged at any time. 9. 2 “Investment Learning or “Mixture-of-Expertise” Architecture 127 9. 2 “Investment Learning or “Mixture-of-Expertise”ArchitectureHere, we approach a solution in a modular way ... 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 Part 1 docx
... when a bellJ. Walter Rapid Learning in Robotics 1Die Deutsche Bibliothek — CIP Data Walter, Jörg Rapid Learning in Robotics / by Jörg Walter, 1st ed.Göttingen: Cuvillier, 199 6Zugl.: Bielefeld, ... 123 9 “Mixture-of-Expertise” or “Investment Learning 125 9. 1 Context dependent “skills” . . . . . . . . . . . . . . . . . . . 125 9. 2 “Investment Learning or “Mixture-of-Expertise” Architec-ture ... 199 6Zugl.: Bielefeld, Univ., Diss. 199 6ISBN 3-8 95 8 8-7 2 8-5 Copyright:c 199 7, 199 6 for electronic publishing: Jörg Walter Technische Fakultät, Universität Bielefeld, AG NeuroinformatikPBox 100131, 33615...
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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 ... degrees-of-freedom robot manipulator in conjunction with a multi-fingered robot hand.The compromise solution between a mature robot, which is able toJ. Walter Rapid Learning in Robotics 9 5It...
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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 4 pdf
... cooperative learning in order to increase speed and ro-bustness of learning, studied e.g. in Walter, Martinetz, and Schulten( 199 1) and compared to the so-called Neural-Gas Network in Walter ( 199 1) ... 198 9; Walter and Schulten 199 3). In time-seriesprediction it was introduced in conjunction with the SOM (Walter, Ritter,and Schulten 199 0) and later with the Neural-Gas network (Walter 199 1;Martinetz ... 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....
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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 7 29 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 00102030 90 100110120130140150160xyzr ... inputs. All remaining results are obtainedusing only four (non-coplanar) points as inputs.104 Application Examples in the Vision Domain(Kummert et al. 199 3b).To apply the PSOM-approach to this ... three worst cases in the test set(remaining images).Chapter 8Application Examples in the Robotics DomainAs pointed out before in the introduction, in the robotic domain the avail-ability of sensorimotor...
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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. ... retina coordinates (2D in each camera) . Thus the training vectorsfor the construction of the T-PSOM are the tuples . 9. 3 Examples 137 In the investing pre-training phase, nine mappings are learned ... 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 11 pps
... Martinetz, and K. J. Schulten ( 198 9). Topology-conserving maps for learning visuo-motor-coordination. Neural Net-works 2, 1 59 168.Rosenblatt, F. ( 196 2). Principles of Neurodynamics. Spartan, ... De-partment der Technische Universität München. Walter, J. ( 199 6). SORMA: Interoperating distributed robotics hardware. In Proc. Int. Conf. on Robotics and Automation (ICRA -9 7 ), pp. (submit-ted). Walter, ... brain damage. In D. Touretzky (Ed.), NIPS* 89, Volume 2, pp. 598 –605. Morgan Kauf-mann.J. Walter Rapid Learning in Robotics 147152 BIBLIOGRAPHYMontana, D. and L. Davis ( 198 9). Training feedforward...
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rapid learning in robotics jorg walter pot
... cooperative learning in order to increase speed and ro-bustness of learning, studied e.g. in Walter, Martinetz, and Schulten( 199 1) and compared to the so-called Neural-Gas Network in Walter ( 199 1) ... 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 ... 123 9 “Mixture-of-Expertise” or “Investment Learning 125 9. 1 Context dependent “skills” . . . . . . . . . . . . . . . . . . . 125 9. 2 “Investment Learning or “Mixture-of-Expertise” Architec-ture...
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