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 ... growing “remoteness” to the trained mapping area. This property limits the extrapolation abilities of the PSOM, depending on the particular distribution of training data. The beginnin...

Ngày tải lên: 10/08/2014, 02:20

... when a bell J. Walter Rapid 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, ... . . 1 06 8.1 [a–d] Kinematic workspace of the TUM robot finger . . . . . 108 8.2 [a–e] Training and testing of the finger kinematics PSOM . . 110 Jörg A. Walter Rapid Lea...

Ngày tải lên: 10/08/2014, 02:20

... 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...

Ngày tải lên: 10/08/2014, 02:20

... 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...

Ngày tải lên: 10/08/2014, 02:20

... 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. ... 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...

Ngày tải lên: 10/08/2014, 02:20

... augmenting 729 joint angle vectors on a rectangular 3 3 3 3 3 3 grid in joint angle space with the missing – 112 Application Examples in the Robotics Domain -4 0 -3 0 -2 0 -1 0 0 10 20 30 40 -4 0 -3 0 -2 0 -1 0 0 10 20 3 0 90 100 110 120 130 140 150 160 x y z r ... three worst cases in the test set (remaining images). Chapter 8 Application Examples in the...

Ngày tải lên: 10/08/2014, 02:20

... Application Examples in the Robotics Domain 2. What is the in uence of standard and Chebyshev-spaced sampling of training points inside their working interval? When the data val- ues (here 3 per ... 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...

Ngày tải lên: 10/08/2014, 02:20

... efficient learning modules for the continuous and smooth mapping domain, the “mixture-of-expert” scheme is superior in managing mapping domains which require non-continuous or non-smooth interfaces. ... random lo- cations (from within the range of the training set) seen in 10 different 138 “Mixture-of-Expertise” or “Investment Learning camera setups, from within the square grid of the...

Ngày tải lên: 10/08/2014, 02:20

... Ad- vances in Neural Information Processing Systems 8 (NIPS*95), pp. 570– 5 76. Bradford MIT Press. Walter, J. and H. Ritter (1996c). The NI robotics laboratory. Technical Report SFB 360 -TR-9 6- 4 , ... SFB 360 -TR-9 6- 4 , TF-AG-NI, Universität Bielefeld, D-3 361 5 Bielefeld. Walter, J. and H. Ritter (1996d). Rapid learning with parametrized self- organizing maps. Neurocomputing...

Ngày tải lên: 10/08/2014, 02:20

... 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...

Ngày tải lên: 27/06/2014, 18:20