Abstract
Using reinforcement learning has enabled robots to learn how to accomplish a wide range of tasks without explicit instructions. In this paper, we use a single-arm robot for the flattening of a piece of cloth which is crumpled and placed on a table. We create a simulation environment with a gripper and a piece of cloth to learn a policy for the robot to choose the best action based on the observation of the environment. The policy is then transferred to a real robot and successfully tested. We also introduce our method on the recognition of the corners of the cloth using computer vision which includes comparing classic computer vision approach to a deep learning one. We use an ABB robot and a 2D camera for the experiments and PyBullet software for the simulation.
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References
Polydoros, A.S., Nalpantidis, L.: Survey of model-based reinforcement learning: applications on robotics. J. Intel. Robot. Syst. 86(2), 153173 (2017)
Mariolis, I., Peleka, G., Kargakos, A., Malassiotis, S.: Pose and category recognition of highly deformable objects using deep learning. In: IEEE International Conference on Advanced Robotics (ICAR), pp. 655–662 (2015)
Yuan, W., Mo, Y., Wang, S., Adelson, E.H.: Active clothing material perception using tactile sensing and deep learning. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4842–4849 (2018)
Saxena, K., Shibata, T.: Garment recognition and grasping point detection for clothing assistance task using deep learning. IEEE/SICE International Symposium on System Integration (SII), pp. 632–637 (2019)
Doumanoglou, A., Stria, J., Peleka, G., Mariolis, I., Petrik, V., Kargakos, A., Wagner, L., Hlavac, V., Kim, T.K., Malassiotis, S.: Folding clothes autonomously: a complete pipeline. IEEE Trans. Robot. 32(6), 1461–1478 (2016)
Yamazaki, K., Oya, R., Nagahama, K., Okada, K., Inaba, M.: Bottom dressing by a life-sized humanoid robot provided failure detection and recovery functions. In: 2014 IEEE/SICE International Symposium on System Integration. SII, vol. 2014, pp. 564–570 (2014)
Yamazaki, K., Ueda, R., Nozawa, S., Mori, Y., Maki, T., Hatao, N., Okada, K., Inaba, M.: System integration of a daily assistive robot and its application to tidying and cleaning rooms. In: IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010–Conference Proceedings, pp. 1365–1371 (2010)
Stria, J., Prusa, D., Hlavac, V., Wagner, L., Petrik, V., Krsek, P., Smutny, V.: Garment perception and its folding using a dual-arm robot. In: IEEE International Conference on Intelligent Robots and Systems, pp. 61–67 (2014)
Petrík, V., Smutný, V., Krsek, P., Hlaváč, V.: Single arm robotic garment folding path generation. Adv. Robot. 31(23–24), 1325–1337 (2017)
Yamazaki, K., Nagahama, K., Inaba, M.: Daily clothes observation from visible surfaces based on wrinkle and cloth-overlap detection. In: Proceedings of the 12th IAPR Conference on Machine Vision Applications, MVA 2011, pp. 275–278 (2011)
Sun, L., Aragon-camarasa, G., Rogers, S., Siebert, J.P.: Autonomous clothes manipulation using a hierarchical vision architecture. IEEE Access 6, 76646–76662 (2018)
Clegg, A., Yu, W., Erickson, Z., Tan, J., Liu, C., Turk, G.: Learning to navigate cloth using haptics. In: IEEE International Conference on Intelligent Robots and Systems, pp. 2799–2805. Institute of Electrical and Electronics Engineers Inc. (2017)
Yuan, W., Mo, Y., Wang, S., Adelson, E.H.: Active clothing material perception using tactile sensing and deep learning. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 4842–4849. IEEE (2018)
Borrás, J., Alenyá, G., Torras, C.: A grasping-centered analysis for cloth manipulation. IEEE Trans. Robot. 36(3), 924–936 (2020)
Lakshmanan, K., Sachdev, A., Xie, Z., Berenson, D., Goldberg, K., Abbeel, P.: Constraint-Aware Motion Planning Algorithm A, for Robotic Folding of Clothes, Experimental Robotics. Springer Tracts in Advanced Robotics, vol. 88. Springer, Heidelberg (2013)
Willimon, B., Birchfield, S., Walker, I.: Model for Unfolding Laundry Using Interactive Perception pp. 4871–4876. IROS (2011)
Sun, L., Aragon-Camarasa, G., Cocshott, P., Rogers, S., Siebert, J.P.: A Heuristic-Based Approach for Flattening Wrinkled Clothes. TAROS (2013)
Sun, L., Aragon-Camarasa, G., Rogers, S., Siebert, J.P.: Accurate garment surface analysis using an active stereo robot head with application to dual- arm flattening. Robot. Autom. (ICRA) 185–192 (2015)
Sun, L., Camarasa, G.A., Khan, A., Rogers, S., Siebert, P.: A precise method for cloth configuration parsing applied to single-arm flattening. Int. J. Adv. Robot. Syst. 13(2) (2016)
Jia, B., Pan, Z., Hu, Z., Pan, J., Manocha, D.: Cloth manipulation using random-forest-based imitation learning. IEEE Robot. Autom. Lett. 4(2), 2086–93 (2019)
Tamei, T., Matsubara, T., Rai, A., Shibata, T.: Reinforcement learning of clothing assistance with a dual-arm robot. In: IEEE-RAS International Conference on Humanoid Robots, pp. 733–738 (2011)
Matsubara, T.: Reinforcement learning of motor skills with non-rigid materials using topology coordinates. Adv. Robot. 27(7), 513–524 (2013)
Koganti, N., Tamei, T., Matsubara, T., Shibata, T.: Real-time estimation of human-cloth topological relationship using depth sensor for robotic clothing assistance. In: IEEE International Workshop on Robot and Human Interactive Communication, pp. 124–129 (2014)
Matas, J., James, S., Davison, A.J.: Sim-to-real reinforcement learning for deformable object manipulation. In: 2nd Conference on Robot Learning (CoRL 2018) (2018)
Wu, Y., Yan, W., Kurutach, T., Pinto, L., Abbeel, P.: Learning to Manipulate Deformable Objects without Demon-Strations (2019). ArXiv:1910.13439
Lillicrap, T.P., Hunt, J.J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., Wierstra, D.: Continuous control with deep reinforcement learning. In: CoRR (2015)
Silver, D. et al.: Deterministic policy gradient algorithms. In: International Conference on Machine Learning (2014)
Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference (1988)
Věcerík, M., Hester, T., Scholz, J., Wang, F., Pietquin, O., Piot, B., Heess, N., Rothörl, T., Lampe, T., Riedmiller, M.: Leveraging demonstrations for deep reinforcement learning on robotics problems with sparse rewards. In: CoRR (2017)
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Shehawy, H., Pareyson, D., Caruso, V., Zanchettin, A.M., Rocco, P. (2023). Flattening Clothes with a Single-Arm Robot Based on Reinforcement Learning. In: Petrovic, I., Menegatti, E., Marković, I. (eds) Intelligent Autonomous Systems 17. IAS 2022. Lecture Notes in Networks and Systems, vol 577. Springer, Cham. https://doi.org/10.1007/978-3-031-22216-0_39
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