Reverse TDNN: An Architecture For Trajectory Generation

Trajectory generation finds interesting applications in the field of robotics, automation, filtering,or time series prediction. Neural networks, with their ability to learn from examples, have been proposed very early on for solving nonlinear control problems adaptively.Several neural net architectures have been proposed for trajectory generation, most notably recurrent networks, either with discrete time and externalloops (Jordan,1986), or with continuous time (Pearlmutter, 1988). Aside from being recurrent, these networks are not specifically tailored for trajectory generation. Ithas been shown that specific architectures, such as the Time Delay Neural Networks (Lang and Hinton, 1988), or convolutional networks in general, are better than fully connected networks at recognizing time sequences such as speech (Waibel et al., 1989), or pen trajectories (Guyon et al., 1991). We show that special architectures canalso be devised for trajectory generation, with dramatic performance improvement.