A robot ping-pong system which attains good performance in a complex nonlinear environment subject to tight temporal constraints is described. It is shown how the expert controller combines approximate estimates and feedback to arrive at a suitable task plan, emphasizing feasibility rather than strict optimality. The characteristics of the expert controller that cause it to work and their relationship to other tasks are discussed. Particular attention is given to the use of task redundancy, continual integration of new sensor data, continual improvement of the task plan, fast approximate methods to generate values, physics-based models to ensure accuracy, global exception handling, flexible internal architecture and robustness to failure.<>