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.