  Approaching robotic cloth manipulation using reinforcement learning based on
visual feedback is appealing as robot perception and control can be learned
simultaneously. However, major challenges result due to the intricate dynamics
of cloth and the high dimensionality of the corresponding states, what shadows
the practicality of the idea. To tackle these issues, we propose TraKDis, a
novel Transformer-based Knowledge Distillation approach that decomposes the
visual reinforcement learning problem into two distinct stages. In the first
stage, a privileged agent is trained, which possesses complete knowledge of the
cloth state information. This privileged agent acts as a teacher, providing
valuable guidance and training signals for subsequent stages. The second stage
involves a knowledge distillation procedure, where the knowledge acquired by
the privileged agent is transferred to a vision-based agent by leveraging
pre-trained state estimation and weight initialization. TraKDis demonstrates
better performance when compared to state-of-the-art RL techniques, showing a
higher performance of 21.9%, 13.8%, and 8.3% in cloth folding tasks in
simulation. Furthermore, to validate robustness, we evaluate the agent in a
noisy environment; the results indicate its ability to handle and adapt to
environmental uncertainties effectively. Real robot experiments are also
conducted to showcase the efficiency of our method in real-world scenarios.
