Rapid Learning in Robotics - Jorg Walter Part 3 ppsx

... versus On-line Learning and Interferences: Off-line learning al-lows easier control of the training procedure and validity of the data(identification of outliers). On-line, incremental learning is ... VariablesNeural Networks Learning LearningMachine Learning Sub-symbolic & Fuzzy Learning LearningMathematics Approximation QuantizationStatistics Regression ClassificationEngineering System Identification ... of an early learning algorithm for theone-layer Perceptron. The learning algorithm described a way of itera-tively changing the weights.J. Walter Rapid Learning in Robotics 23 28 Artificial...

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...

Rapid Learning in Robotics - Jorg Walter Part 1 docx

... example, 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, 1996Zugl.: ... . . . . . . . . . . 131 9 .3. 2 Rapid Visuo-motor Coordination Learning . . . . . . 132 9 .3. 3 Factorize Learning: The 3 D Stereo Case . . . . . . . . 136 10 Summary 139 Bibliography 146viii CONTENTS ... 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...

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 ... in the Kohonen mapto a continuous auxiliary mapping or parameter manifold in theJ. Walter Rapid Learning in Robotics 43 3.6 Selecting the Right Network Size 37 tween neighboring neurons in ... 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”...

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 ... example, for positioning a robot manipulator at a particular posi-tion in the 3 D workspace, the 6 degress-of-freedom (DOF) of the manipu-lator are under-constrained. There are in nite many solutions...

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 001020 30 40 -4 0 -3 0 -2 0 -1 001020 3 090100110120 130 140150160xyzr ... Kinematics Mapping 1 13 -4 0 -3 0 -2 0 -1 001020 30 40 -4 0 -3 0 -2 0 -1 001020 30 90100110120 130 140150160xyzr 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...

Rapid Learning in Robotics - Jorg Walter Part 9 docx

... a 3 3 3 3 3 3C-PSOM. The six-dimensional man-ifold is embedded in a 15-dimensional-space.The spatial distribution of the resulting deviations is displayed in Fig. 8.7 (of the third case in ... mm 0.041 3 3 3 C-PSOM 11 mm 0.02744 4 PSOM 2.4 mm 0.006144 4 C-PSOM 1.7 mm 0.004255 5 PSOM 0.11 mm 0.0002755 5 C-PSOM 0.091 mm 0.000 23 3 3 3 L-PSOM of 4 4 4 6.7 mm 0.041 3 3 3 L-PSOM of ... 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 axis)...

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. ... 1987)on the basis of the values obtained from the Meta-PSOM. 136 “Mixture-of-Expertise” or “Investment Learning is visible in Tab. 9.2.9 .3. 3 Factorize Learning: The 3 D Stereo CaseThe next step ... a 3 3 3 grid in of sizecm ) jointly with the joint angle tuple and the location in cam-era retina coordinates (2D in each camera) . Thus the training vectorsfor the construction of the T-PSOM...

Rapid Learning in Robotics - Jorg Walter Part 11 pps

... Technical ReportSFB360-TR-9 6 -3 , Universität Bielefeld, D -3 3 615 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 -3 3 615Bielefeld. Walter, J. and H. Ritter (1996d). Rapid learning with parametrized self-organizing maps. Neurocomputing 12, 131 –1 53. Walter, J. and ... Proc. Int. Conf.on Artiﬁcial Neural Networks (ICANN-91), Espoo, Finland, pp. 4 03 408.North-Holland, Amsterdam.Fritzke, B. (1995). Incremenal learning of local linear mappings. In Proc.Int....

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 ... . . 131 9 .3. 2 Rapid Visuo-motor Coordination Learning . . . . . . 132 9 .3. 3 Factorize Learning: The 3 D Stereo Case . . . . . . . . 136 10 Summary 139 Bibliography 146Chapter 2The Robotics LaboratoryThis ... 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...

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