  In pursuit of the time-optimal path tracking (TOPT) trajectory of a robot
manipulator along a preset path, a beforehand identified robot dynamic model is
usually used to obtain the required optimal trajectory for perfect tracking.
However, due to the inevitable model-plant mismatch, there may be a big error
between the actually measured torques and the calculated torques by the dynamic
model, which causes the obtained trajectory to be suboptimal or even be
infeasible by exceeding given limits. This paper presents a TOPT-oriented SARSA
algorithm (TOPTO-SARSA) and a two-step method for finding the time-optimal
motion and ensuring the feasibility : Firstly, using TOPTO-SARSA to find a safe
trajectory that satisfies the kinematic constraints through the interaction
between reinforcement learning agent and kinematic model. Secondly, using
TOPTO-SARSA to find the optimal trajectory through the interaction between the
agent and the real world, and assure the actually measured torques satisfy the
given limits at the last interaction. The effectiveness of the proposed
algorithm has been verified through experiments on a 6-DOF robot manipulator.
