A neural dynamics to organize timed movement: Demonstration in a robot ball bouncing task

To address how different movement behaviors may be timed to sensory events while being flexibly organized in sequence, we propose a neural dynamic model of timed movement organization. Two layers of neural dynamics control the activation and de-activation of different elementary movements, while a third layer uses stable limit cycle oscillators to generate timed movement trajectories. Both the organization and the generation of the timed movements are coupled to online sensory information so that the system can compensate for perturbations by updating the movement trajectory while recovering the required movement timing. We formulate and demonstrate the approach in a robotic ball bouncing task. When the ball begins to fall, the robot arm moves to the interception point in a plane and initiates a rotatory motion of the racket timed such as to hit the ball with maximal velocity. When the ball is no longer falling or falling outside the reachable space, the robot moves the racket back toward baseline. A physics simulation is used to assess the model and demonstrate its capacity to handle perturbations of the ball trajectory.