# Simpler Learning of Robotic Manipulation of Clothing by Utilizing DIY Smart Textile Technology
## 2 Related Work  
When calculating the required trajectories for deformable object manipulation, the deformations need to be taken into account [5]. One approach to incorporating this is by using simulation. In [16], for example, deformations of a linear flexible object are approximated in a simulation in order to generate trajectories for folding in the air. In the case of clothing, the prior work presented in [7; 8] dealt with the high-dimensional clothing state by building folding processes in which grasping points are manually chosen so that wrinkles are minimized. Next, the executed manipulation trajectories are hard-coded or calculated by matching polygonal models to contours. However, these approaches can accumulate errors across stages. For example, the type of textile can be mislabelled early in the process, leading to subsequent incorrect pipeline operations [8]. Additionally, engineered robotic pipelines for the folding task can take up to 24 min to execute [7], with the grasp point detection phase being the largest bottleneck. In [17], a faster process was implemented which could grasp and classify clothing in one minute. There were two overhead cameras in their setup, but the view was occluded when the arm engages. Consequently, the grasping procedure had to be executed repeatedly at different heights in order to detect whether an isolated garment was grasped. To avoid highly-engineered pipelines and the associated loss of information between the stages, more recent methods utilize learning methods in order to grasp or manipulate deformable objects. For example, in [9], deep reinforcement learning is used to train a neural network to learn to manipulate a towel. The controller is trained in simulation and transferred to the real platform using domain randomization. In their approach, the agent learns from the pixel input only, while  
Figure 1: Schematic overview of the setup: dual robot arms hold a smart textile piece and have to learn to fold it from scratch using sensory feedback of the textile and proprioceptive input of the robotic arms. our method leverages tactile sensing to attain a more informational representation of the cloth. In [18], a forward dynamics model of the cloth using a latent space representation is learned. This architecture is used in [19] to bring cloth into the desired configuration. However, transferring this to the real world is challenging due to the difficulty in generating high-fidelity rollouts in simulation [9; 19]. In our work, the policy of the robot is learned to avoid sim-to-real transfer issues. Other work tries to overcome the many training demonstrations required for training by utilizing learning from demonstration methods in order to speed up the training process [20; 21]. This was illustrated in [22], who used teleoperated demonstrations to teach a low-cost robotic setup multiple tasks. However, general-purpose, dual-armed robots typically contain many degrees of freedom (e.g., the humanoid dual-arm Baxter(r) robot contains seven degrees of freedom per arm [23]), making teleoperation difficult. Defining a reward function for the robotic folding task is difficult because it requires the state estimation of the cloth. Previous work [8; 24] has extracted the contour of the textile using color segmentation. In [20], a marked towel is tracked, allowing the calculation of the distance between points on the towel from the training sample and the example demonstrations so that it can be used as a reward for the agent. Their method requires prior information about the shape of the object in order to reconstruct the missing market points. More recent work in using (deep) reinforcement learning for robotic folding also used vision-based methods to define the reward function for the agent [9; 12]. However, relying solely on visual inputs and marker clues does not scale well. Another approach to find a reward function was explored in [25; 26], where inverse reinforcement learning (IRL) was used to learn a reward function from expert demonstrations.