Controlling multifingered robot hands makes high demands on the control algorithms and the speed of the control computer. The nonlinear friction, the impact problem and other plant uncertainties require a special kind of control and tuning of the controller. Some simple linear and nonlinear controllers for the Karlsruhe Dexterous Hand are presented and the results and advantages of the controllers are shown. Also, a new adaptive fuzzy controller is presented to overcome the time consuming process of fine-tuning the membership functions. Finally, a short glance at the hardware platform is taken in order to show the control system architecture.

Locally optimum vector quantizer (VQ) designs are presented for memoryless Gaussian, gamma, and Laplacian sources. For Gaussian sources, low (2-6) dimensional vector quantization provides relatively little improvement in mean-squared error (MSE) compared to the minimum mean-squared error (MMSE) scalar quantizer. For Laplacian or gamma sources, however, significant improvement in MSE is available with vector quantization. The Laplacian and gamma 6 bit, sixdimensional vector quantizers achieve, respectively, improvements of 2 and 4.5 dB over the corresponding scalar MMSE quantizer distortions.