Rapid Learning in Robotics - Jorg Walter Part 4 pdf
Rapid Learning in Robotics - Jorg Walter Part 4 pdf
... position.More training data: Over-fitting can be avoided when sufficient trainingpoints are available, e.g. by learning on-line. Duplicating the avail-able training data set and adding a small amount ... 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) ... the LLM-outputs of several neurons, in- stead of considering one single neuron. This can be achieved by replac-ing the “winner-takes-all” rule (Eq. 3.9) with a “winner-takes-most” or “soft-max”...
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Rapid Learning in Robotics - Jorg Walter Part 1 docx
... walter@ techfak.uni-bielefeld.deUrl: http://www.techfak.uni-bielefeld.de/ walter/ c1997 for hard copy publishing: Cuvillier VerlagNonnenstieg 8, D-37075 Göttingen, Germany, Fax: +4 9-5 5 1-5 47 2 4- 2 1ivvi ... 1068.1 [a–d] Kinematic workspace of the TUM robot finger . . . . . 1088.2 [a–e] Training and testing of the finger kinematics PSOM . . 110Jörg A. Walter Rapid Learning in Robotics Robotics deals ... . . . . 50 4. 8 PSOM learning from scratch . . . . . . . . . . . . . . . . . . . 54 4.9 The modified adaptation rule Eq. 4. 15 . . . . . . . . . . . . . 56J. Walter Rapid Learning in Robotics ...
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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 6 pot
... continuous manifold in . However, in certain cases input-output mappings are non-continuous. The particularmanifold shape in conjunction with the associative completion and its op-tional partial ... growing “remoteness” to the trained mapping area. This propertylimits the extrapolation abilities of the PSOM, depending on the particulardistribution of training data. The beginning in- folding ... 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...
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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 00102030 40 -4 0 -3 0 -2 0 -1 0010203090100110120130 140 150160xyzr ... Kinematics Mapping 113 -4 0 -3 0 -2 0 -1 00102030 40 -4 0 -3 0 -2 0 -1 0010203090100110120130 140 150160xyzr s1 s2 A∈S wa a θ Figure 8.5: The same 27 training data vectors ... 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 9 docx
... mm 0. 041 33 3 C-PSOM 11 mm 0.027 4 4 4 PSOM 2 .4 mm 0.0061 4 4 4 C-PSOM 1.7 mm 0.0 042 55 5 PSOM 0.11 mm 0.0002755 5 C-PSOM 0.091 mm 0.0002333 3 L-PSOM of 4 4 4 6.7 mm 0. 041 33 3 L-PSOM ... 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 ... 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...
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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 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 ... Proc. Int. Conf.on Artificial Neural Networks (ICANN-91), Espoo, Finland, pp. 40 3 40 8.North-Holland, Amsterdam.Fritzke, B. (1995). Incremenal learning of local linear mappings. In Proc.Int. ... self-organizing map. In Proc. IEEE, Volume 78,pp. 146 4– 148 0.Kohonen, T. (1995). Self-Organizing Maps, Volume 30 of Springer Series in Information Sciences. Berlin, Heidelberg: Springer.Kummert,...
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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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