One night in 1982, John Mumford was working on an avalanche patrol on an icy Colorado mountain pass when the van carrying him and two other men slid off the road and plunged over a cliff. The other guys were able to walk away, but Mumford had broken his neck. The lower half of his body was paralyzed, and though he could bend his arms at the elbows, he could no longer grasp things in his hands. Fifteen years later, however, he received a technological wonder that reactivated his left hand. It was known as the Freehand System. A surgeon placed a sensor on Mumford's right shoulder, implanted a pacemaker-size device known as a stimulator just below the skin on his upper chest, and threaded wires into the muscles of his left arm.

Within a simple test-bed, application of feed-forward neurocontrol for short-term planning of robot trajectories in a dynamic environment is studied. The action network is embedded in a sensorymotoric system architecture that contains a separate world model. It is continuously fed with short-term predicted spatiotemporal obstacle trajectories, and receives robot state feedback. The action net allows for external switching between alternative planning tasks. It generates goal-directed motor actions - subject to the robot's kinematic and dynamic constraints - such that collisions with moving obstacles are avoided.

Within a simple test-bed, application of feed-forward neurocontrol for short-term planning of robot trajectories in a dynamic environment is studied. The action network is embedded in a sensorymotoric system architecture that contains a separate world model. It is continuously fed with short-term predicted spatiotemporal obstacle trajectories, and receives robot state feedback. The action net allows for external switching between alternative planning tasks. It generates goal-directed motor actions - subject to the robot's kinematic and dynamic constraints - such that collisions with moving obstacles are avoided.

The action network is embedded in a sensorymotoric systemarchitecture that contains a separate world model. It is continuously fed with short-term predicted spatiotemporal obstacle trajectories, and receives robot state feedback. The action netallows for external switching between alternative planning tasks.It generates goal-directed motor actions - subject to the robot's kinematic and dynamic constraints - such that collisions withmoving obstacles are avoided. Using supervised learning, we distribute examples of the optimal planner mapping over a structure-level adapted parsimonious higher order network. The training database is generated by a Dynamic Programming algorithm. Extensivesimulations reveal, that the local planner mapping is highly nonlinear, but can be effectively and sparsely represented bythe chosen powerful net model. Excellent generalization occurs for unseen obstacle configurations. We also discuss the limitations offeed-forward neurocontrol for growing planning horizons.