We present an attractor based dynamics that autonomously generates trajectories with stable timing
(limit cycle solutions), stably adapted to changing online sensory information. Autonomous differential
equations are used to formulate a dynamical layer with either stable fixed points or a stable limit cycle.
A neural competitive dynamics switches between these two regimes according to sensorial context
and logical conditions. The corresponding movement states are then converted by simple coordinate
transformations and an inverse kinematics controller into spatial positions of a robot arm. Movement
initiation and termination is entirely sensor driven. In this article, the dynamic architecture was changed
in order to cope with unreliable sensor information by including this information in the vector field.
We apply this architecture to generate timed trajectories for a Puma arm which must catch a moving
ball before it falls over a table, and return to a reference position thereafter. Sensory information is
provided by a camera mounted on the ceiling over the robot. A flexible behavior is achieved. Flexibility
means that if the sensorial context changes such that the previously generated sequence is no longer
adequate, a new sequence of behaviors, depending on the point at which the changed occurred and
adequate to the current situation emerges.
The evaluation results illustrate the stability and flexibility properties of the dynamical architecture
as well as the robustness of the decision-making mechanism implemented