  We present a novel approach for robust manipulation of high-DOF deformable
objects such as cloth. Our approach uses a random forest-based controller that
maps the observed visual features of the cloth to an optimal control action of
the manipulator. The topological structure of this random forest-based
controller is determined automatically based on the training data consisting
visual features and optimal control actions. This enables us to integrate the
overall process of training data classification and controller optimization
into an imitation learning (IL) approach. Our approach enables learning of
robust control policy for cloth manipulation with guarantees on convergence.We
have evaluated our approach on different multi-task cloth manipulation
benchmarks such as flattening, folding and twisting. In practice, our approach
works well with different deformable features learned based on the specific
task or deep learning. Moreover, our controller outperforms a simple or
piecewise linear controller in terms of robustness to noise. In addition, our
approach is easy to implement and does not require much parameter tuning.
