Tactile-Based Self-supervised Pose Estimation for Robust Grasping
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-030-71151-1_25
Reference13 articles.
1. Calandra, R., Owens, A., Upadhyaya, M., Yuan, W., Lin, J., Adelson, E.H., Levine, S.: The feeling of success: does touch sensing help predict grasp outcomes? IEEE Robot. Autom. Lett. (RA-L) 3(4), 3300–3307 (2017)
2. Liarokapis, M.V., Calli, B., Spiers, A.J., Dollar, A.M.: Unplanned, model-free, single grasp object classification with underactuated hands and force sensors. In: IEEE International Conference on Intelligent Robots and Systems (IROS), vol. 2015, pp. 5073–5080. Institute of Electrical and Electronics Engineers Inc. (2015)
3. Kwiatkowski, J., Lavertu, J.S., Gourrat, C., Duchaine, V.: Determining object properties from tactile events during grasp failure. In: IEEE International Conference on Automation Science and Engineering (CASE), vol. 2019, pp. 1692–1698 (2019)
4. Chebotar, Y., Hausman, K., Su, Z., Sukhatme, G.S., Schaal, S.: Self-supervised regrasping using spatio-temporal tactile features and reinforcement learning. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1960–1966 (2016)
5. Liarokapis, M., Dollar, A.M.: Learning the post-contact reconfiguration of the hand object system for adaptive grasping mechanisms. In: IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 293–299 (2017)
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