Remembering how to behave: Recurrent neural networks for adaptive robot behavior.

Abstract

this paper, a network of the former type will be analyzed in the following. Figure 24 shows a characteristic trajectory of a successful robot controller of this type. As above, the robot starts off facing the upper left obstacle. It turns away from it to the left, enters the zone, and collects three objects on its first pass through the zone, turning slightly to the left towards each of them. As soon as it has left the zone it starts moving in a semi-circle to the left, which takes it back into the zone. In the zone it starts moving straight ahead again, takes a slight turn to the right to collect the upper object, and continues straight ahead out of the zone. The same pattern is repeated: as soon as it leaves the zone, it moves in a semi-circle to the left, which takes it back into the zone, where it starts moving straight forward again. Once more it performs a slight turn to the right to collect an object it would otherwise have missed. It continues to move straight ahead, leaves the zone, returns in another semi-circle, enters once more and moves straight ahead until the evaluation period ends.