Margit Kinder

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A neural classiier of planar trajectories is presented. There already exist a large variety of classi-ers that are specialized on particular invariants contained in a trajectory classiication task such as position-invariance, rotation-invariance, size-invariance, .... That is, there exist classiiers specialized on recognizing trajectories e.g. independently(More)
This paper presents a novel kind of generalizing neural storage tailored for the problem of global motion planning for six-joint manipulators in complex, changing environments. Paths are stored in a growing map of neurons with adaptive ellipsoidal receptive eld, called the Ellipsoidal Map. Path planning is based on nding the best-matching neurons for start(More)
We aim at building a joint space trajectory generation system. Connected to a xed ma-nipulator with sensory feedback, neural networks are expected to move the end-eeector from any start to any goal connguration without colliding with obstacles. The output of our system is a series of consecutive conngurations yielding a joint-space trajectory. Sensory(More)
We present a neural trajectory storage for manipulator trajectories. Trajectories will be neurally stored as a sequence of discrete joint positions. Every time a trajectory needs to be generated, the storage is consulted. Besides being a \classical" retrieval system, the neural network is able to generalize from stored trajectories to new trajectories and(More)
There exist all kinds of problems where both input and output data for neural networks are continuous and vector-valued. From our previous works we know that distributed representations of the input data are extremely useful for neural networks to embody good generalization skills and also to model forbidden regions in data space. Units are only located in(More)
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