Precise task-space tracking with manipulator-type systems requires accurate kinematics models. In contrast to traditional manipulators, it is difficult to obtain an accurate kinematic model of humanoid robots due to complex structure and link flexibility. Also, prolonged use of the robot will lead to some parts wearing out or being replaced with a slightly different alignment, thus throwing off the initial calibration. Therefore, there is a need to develop a control algorithm that can compensate for the modeling errors and quickly retune itself, if needed, taking into account the controller bandwidth limitations and high dimensionality of the system. In this paper, we develop an iterative learning control algorithm that can work with existing inverse kinematics solver to refine the joint-level control commands to enable precise tracking in the task space. We demonstrate the efficacy of the algorithm on a theme-park type humanoid that learns to track the figure eight in 18 trials and to serve a drink without spilling in 9 trials.
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