Rapid Learning in Robotics - Jorg Walter Part 1 docx
Rapid Learning in Robotics - Jorg Walter Part 1 docx
... Learning in Robotics 1 Die Deutsche Bibliothek — CIP Data Walter, Jörg Rapid Learning in Robotics / by Jörg Walter, 1st ed.Göttingen: Cuvillier, 19 96Zugl.: Bielefeld, Univ., Diss. 19 96ISBN 3-8 958 8-7 2 8-5 Copyright:c 19 97, ... example . . . . . 12 18 .10 [a–d] Intermediate steps in optimizing the mobility reserve 12 18 .11 [a–d] The PSOM resolves redundancies by extra constraints 12 39 .1 Context dependent mapping tasks . . ... rule Eq. 4 .15 . . . . . . . . . . . . . 56J. Walter Rapid Learning in Robotics ixxii LIST OF FIGURESCONTENTS vii8 Application Examples in the Robotics Domain 10 78 .1 Robot Finger Kinematics...
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Rapid Learning in Robotics - Jorg Walter Part 9 docx
... are indicated 11 8 Application Examples in the Robotics Domain020406080 10 0 12 0 14 0 16 00 10 0 200 300 400 500 600 700 800Number of Training ExamplesMean Cartesian Deviation [mm]Mean Joint ... Investment Learning Phase Meta-Box c X 1 X2 parameters or weights ω T-Box Prototypical Context (1) (1) (2) (2) Figure 9.2: The Investment Learning Phase. In the investment learning ... mm 0.0 41 33 3 C-PSOM 11 mm 0.02744 4 PSOM 2.4 mm 0.00 61 44 4 C-PSOM 1. 7 mm 0.004255 5 PSOM 0 .11 mm 0.0002755 5 C-PSOM 0.0 91 mm 0.0002333 3 L-PSOM of 4 4 4 6.7 mm 0.0 41 33 3 L-PSOM of...
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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 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. ... (Koho-nen 19 90; Ritter and Kohonen 19 89). The topology preserving prop-erties enables cooperative learning in order to increase speed and ro-bustness of learning, studied e.g. in Walter, Martinetz, ... 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” mechanism. For example, by employing Eq. 3.6 in the index...
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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
... Kinematics Mapping 11 3 -4 0 -3 0 -2 0 -1 00 10 203040 -4 0 -3 0 -2 0 -1 00 10 203090 10 0 11 0 12 0 13 0 14 0 15 0 16 0xyzr s 1 s2 A∈S wa a θ Figure 8.5: The same 27 training data vectors ... augmenting 729 joint angle vectors on a rectangular 3 3 3 3 3 3grid in joint angle space with the missing – 11 2 Application Examples in the Robotics Domain -4 0 -3 0 -2 0 -1 00 10 203040 -4 0 -3 0 -2 0 -1 00 10 203090 10 0 11 0 12 0 13 0 14 0 15 0 16 0xyzr ... Domain -4 0 -3 0 -2 0 -1 00 10 203040 -4 0 -3 0 -2 0 -1 00 10 203090 10 0 11 0 12 0 13 0 14 0 15 0 16 0xyzr θ Figure 8.4: The 27 training data vectors for the Back-propagation networks: (left) in the input spaceand (right) the corresponding...
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Rapid Learning in Robotics - Jorg Walter Part 10 pps
... volume points to be trans-formed into camera coordinates .T-BOX - RMS [L] - RMS [L] - RMS [L](i) ( ) 0.025 0.023 0 .14 (ii) { } 0. 016 0. 015 0 .14 (iii) PSOM 0. 015 0. 014 0 .12 Table 9 .1: Results ... domain, the“mixture-of-expert” scheme is superior in managing mapping domainswhich require non-continuous or non-smooth interfaces. As pointed out,the T-BOX-concept is not restricted to a particular ... averaged over 10 0 random lo-cations (from within the range of the training set) seen in 10 different 13 8 “Mixture-of-Expertise” or “Investment Learning camera setups, from within the square...
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Rapid Learning in Robotics - Jorg Walter Part 11 pps
... ReportSFB360-TR-9 6-3 , Universität Bielefeld, D-33 615 Bielefeld. Walter, J., H. Ritter, and K. Schulten (19 90, June). Non-linear predic-tion with self-organizing maps. In Int. Joint Conf. on ... Press. Walter, J. and H. Ritter (19 96c). The NI robotics laboratory. TechnicalReport SFB360-TR-9 6-4 , TF-AG-NI, Universität Bielefeld, D-33 615 Bielefeld. Walter, J. and H. Ritter (19 96d). Rapid learning ... Conf Proc 15 1, Snowbird, Utah.Ritter, H. J., T. M. Martinetz, and K. J. Schulten (19 89). Topology-conserving maps for learning visuo-motor-coordination. Neural Net-works 2, 15 9 16 8.Rosenblatt,...
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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 (19 91) and compared to the so-called Neural-Gas Network in Walter (19 91) and ... 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 ... . . . 13 19.3.2 Rapid Visuo-motor Coordination Learning . . . . . . 13 29.3.3 Factorize Learning: The 3 D Stereo Case . . . . . . . . 13 6 10 Summary 13 9Bibliography 14 6Chapter 2The Robotics...
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