Rapid Learning in Robotics - Jorg Walter Part 1 docx

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 96 Zugl.: Bielefeld, Univ., Diss. 19 96 ISBN 3-8 958 8-7 2 8-5 Copyright: c 19 97, ... example . . . . . 12 1 8 .10 [a–d] Intermediate steps in optimizing the mobility reserve 12 1 8 .11 [a–d] The PSOM resolves redu...

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Rapid Learning in Robotics - Jorg Walter Part 9 docx

Rapid Learning in Robotics - Jorg Walter Part 9 docx

... are indicated 11 8 Application Examples in the Robotics Domain 0 20 40 60 80 10 0 12 0 14 0 16 0 0 10 0 200 300 400 500 600 700 800 Number of Training Examples Mean Cartesian Deviation [mm] Mean Joint ... Investment Learning Phase Meta-Box c X 1 X 2 parameters or weights ω T-Box P rototypical C ontext (1) (1) (2) (2) Figure 9.2: The Investment Learning Ph...

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Rapid Learning in Robotics - Jorg Walter Part 2 ppsx

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 now adapt most rapidly by “mixi...

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Rapid Learning in Robotics - Jorg Walter Part 3 ppsx

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 t...

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Rapid Learning in Robotics - Jorg Walter Part 4 pdf

Rapid Learning in Robotics - Jorg Walter Part 4 pdf

... training set, but is performing badly on the indicated (cross-marked) position. More training data: Over-fitting can be avoided when sufficient training points 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. i...

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Rapid Learning in Robotics - Jorg Walter Part 6 pot

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 row interpolation ... training data. The beginning in- folding of the map, e.g. seen at the lower left corner in Fig. 5.8 demonstrates further that shows multiple solutions (Eq. 4.4) for finding a best-m...

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Rapid Learning in Robotics - Jorg Walter Part 8 ppt

Rapid Learning in Robotics - Jorg Walter Part 8 ppt

... Kinematics Mapping 11 3 -4 0 -3 0 -2 0 -1 0 0 10 20 30 40 -4 0 -3 0 -2 0 -1 0 0 10 20 30 90 10 0 11 0 12 0 13 0 14 0 15 0 16 0 x y z r s 1 s 2 A∈S w a a θ Figure 8.5: The same 27 training data vectors ... augmenting 729 joint angle vectors on a rectangular 3 3 3 3 3 3 grid in joint angle space with the missing – 11 2 Application Examples in...

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Rapid Learning in Robotics - Jorg Walter Part 10 pps

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 domains which require non-continuous or non-smooth interfaces. As pointed out, the T-B OX-concept is...

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Rapid Learning in Robotics - Jorg Walter Part 11 pps

Rapid Learning in Robotics - Jorg Walter Part 11 pps

... Report SFB360-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. Technical Report SFB360-TR-9 6-4 , TF-AG-NI, Universität Bielefeld, D-33 615 Bielefeld. Walter, J. and H. Ritter...

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rapid learning in robotics jorg walter pot

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...

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