Tactile Estimation of Extrinsic Contact Patch for Stable Placement (2309.14552v2)
Abstract: Precise perception of contact interactions is essential for fine-grained manipulation skills for robots. In this paper, we present the design of feedback skills for robots that must learn to stack complex-shaped objects on top of each other (see Fig.1). To design such a system, a robot should be able to reason about the stability of placement from very gentle contact interactions. Our results demonstrate that it is possible to infer the stability of object placement based on tactile readings during contact formation between the object and its environment. In particular, we estimate the contact patch between a grasped object and its environment using force and tactile observations to estimate the stability of the object during a contact formation. The contact patch could be used to estimate the stability of the object upon release of the grasp. The proposed method is demonstrated in various pairs of objects that are used in a very popular board game.
- M. Riedmiller, R. Hafner, T. Lampe, M. Neunert, J. Degrave, T. van de Wiele, V. Mnih, N. Heess, and J. T. Springenberg, “Learning by playing solving sparse reward tasks from scratch,” in Proceedings of the 5th Conference on Robot Learning (CoRL), ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. PMLR, 10–15 Jul 2018, pp. 4344–4353. [Online]. Available: https://proceedings.mlr.press/v80/riedmiller18a.html
- Y. Zhu, Z. Wang, J. Merel, A. Rusu, T. Erez, S. Cabi, S. Tunyasuvunakool, J. Kramár, R. Hadsell, N. de Freitas, and N. Heess, “Reinforcement and imitation learning for diverse visuomotor skills,” in Proceedings of International Conference on Learning Representations (ICLR), 2018.
- S. Cabi, S. G. Colmenarejo, A. Novikov, K. Konyushkova, S. Reed, R. Jeong, K. Zolna, Y. Aytar, D. Budden, M. Vecerik, O. Sushkov, D. Barker, J. Scholz, M. Denil, N. de Freitas, and Z. Wang, “Scaling data-driven robotics with reward sketching and batch reinforcement learning,” 2020.
- L. Hermann, M. Argus, A. Eitel, A. Amiranashvili, W. Burgard, and T. Brox, “Adaptive curriculum generation from demonstrations for sim-to-real visuomotor control,” in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 6498–6505.
- R. Jeong, Y. Aytar, D. Khosid, Y. Zhou, J. Kay, T. Lampe, K. Bousmalis, and F. Nori, “Self-supervised sim-to-real adaptation for visual robotic manipulation,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2020, pp. 2718–2724.
- M. D. Noseworthy, C. Moses, I. Brand, S. Castro, L. P. Kaelbling, T. Lozano-Pérez, and N. Roy, “Active learning of abstract plan feasibility,” Robotics Science and System (RSS).
- A. X. Lee, C. M. Devin, Y. Zhou, T. Lampe, K. Bousmalis, J. T. Springenberg, A. Byravan, A. Abdolmaleki, N. Gileadi, D. Khosid, C. Fantacci, J. E. Chen, A. Raju, R. Jeong, M. Neunert, A. Laurens, S. Saliceti, F. Casarini, M. Riedmiller, r. hadsell, and F. Nori, “Beyond pick-and-place: Tackling robotic stacking of diverse shapes,” in Proceedings of the 5th Conference on Robot Learning (CoRL), ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 08–11 Nov 2022, pp. 1089–1131. [Online]. Available: https://proceedings.mlr.press/v164/lee22b.html
- F. Furrer, M. Wermelinger, H. Yoshida, F. Gramazio, M. Kohler, R. Siegwart, and M. Hutter, “Autonomous robotic stone stacking with online next best object target pose planning,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 2350–2356.
- Y. Liu, J. Choi, and N. Napp, “Planning for robotic dry stacking with irregular stones,” in Field and Service Robotics, G. Ishigami and K. Yoshida, Eds. Singapore: Springer Singapore, 2021, pp. 321–335.
- O. Kroemer, S. Leischnig, S. Luettgen, and J. Peters, “A kernel-based approach to learning contact distributions for robot manipulation tasks,” Autonomous Robots, vol. 42, pp. 581–600, 2018.
- L. Manuelli and R. Tedrake, “Localizing external contact using proprioceptive sensors: The contact particle filter,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2016, pp. 5062–5069.
- S. Kim, D. K. Jha, D. Romeres, P. Patre, and A. Rodriguez, “Simultaneous tactile estimation and control of extrinsic contact,” arXiv preprint arXiv:2303.03385, 2023.
- D. Ma, S. Dong, and A. Rodriguez, “Extrinsic contact sensing with relative-motion tracking from distributed tactile measurements,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021, pp. 11 262–11 268.
- S. Kim and A. Rodriguez, “Active extrinsic contact sensing: Application to general peg-in-hole insertion,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2022, pp. 10 241–10 247.
- C. Higuera, S. Dong, B. Boots, and M. Mukadam, “Neural contact fields: Tracking extrinsic contact with tactile sensing,” in Proceedings of IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2023, pp. 12 576–12 582.
- K. Ota, D. K. Jha, H.-Y. Tung, and J. B. Tenenbaum, “Tactile-filter: Interactive tactile perception for part mating,” Robotics Science and System (RSS), 2023.
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