Rapid Learning in Robotics - Jorg Walter Part 2 ppsx

... 2. 1, 2. 4). The DLR Force-Torque-Sensor (FTS) wasdeveloped by the robotics group of Prof. Hirzinger of the DLR, Oberpfaf-fenhofen, and is a spin-off from the ROTEX Spacelab mission D2 (Hirzinger,Brunner, ... 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...

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 ... the time for gathering training data is a signiﬁcant issue.It includes also the time for preparing the learning set-up, as well as thetraining phase. Working with robots in reality clearly...

Rapid Learning in Robotics - Jorg Walter Part 1 docx

... 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 ... 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.: ... 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

... 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. ... 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) ... many learning applications desire continuous valuedoutputs. How can the SOM network learn smooth input–output map-pings?Similar to the binning in the hyper-rectangular recursive partitioningalgorithm...

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...

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 –1 12 Application Examples in the Robotics Domain -4 0 -3 0 -2 0 -1 0010 20 3040 -4 0 -3 0 -2 0 -1 0010 20 3090100110 120 130140150160xyzr ... Kinematics Mapping 113 -4 0 -3 0 -2 0 -1 0010 20 3040 -4 0 -3 0 -2 0 -1 0010 20 3090100110 120 130140150160xyzr s1 s 2 A∈S wa a θ Figure 8.5: The same 27 training data vectors ... 0. 12 0.061 0.064 0.0083 0.049 0. 025 150 2 4 2. 6 3 .2 2.8 0.11 0.043 0.048 0.0084 0.0097 0.0077150 3 5 2. 6 3 .2 2.9 0.15 0.0 42 0.047 0.0084 0.0050 0.0045Various distance ranges150 2 2.6 3 .2 2.8...

Rapid Learning in Robotics - Jorg Walter Part 9 docx

... Investment Learning Phase Meta-Box c X1 X 2 parameters or weights ω T-Box Prototypical Context (1) (1) (2) (2) Figure 9 .2: The Investment Learning Phase. In the investment learning ... 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 ... Application Examples in the Robotics Domain 2. 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...

Rapid Learning in Robotics - Jorg Walter Part 10 pps

... emphasizes an important point for the construction of more pow-erful learning systems: in addition to focusing on output value learning, 1 32 “Mixture-of-Expertise” or “Investment Learning The solution ... 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 ... well as the T-BOX/META-BOX approach are very efﬁcient learning modules for the continuous and smooth mapping domain, the“mixture-of-expert” scheme is superior in managing mapping domainswhich...

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 ... Topology-conserving maps for learning visuo-motor-coordination. Neural Net-works 2, 159–168.Rosenblatt, F. (19 62) . Principles of Neurodynamics. Spartan, New York.Rumelhart, D., G. Hinton, ... SFB360-TR-9 6-4 , TF-AG-NI, Universität Bielefeld, D-33615Bielefeld. Walter, J. and H. Ritter (1996d). Rapid learning with parametrized self-organizing maps. Neurocomputing 12, 131–153. Walter, J. and...

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 ... 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 ... 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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