Fetching the paper…
Reading the bibliography…
The sense of touch is an essential ability for skillfully performing a variety of tasks, providing the capacity to search and manipulate objects without relying on visual information.
A. M. Okamura, N. Smaby, and M. R. Cutkosky, “An overview of dexterous manipulation,” in IEEE International Conference on Robotics and Automation. Symposia Proceedings , 2000
2000
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Proceedings of the 27th international conference on machine learning (ICML-10) , 2010, pp. 807–814
2010
Earlier work this paper cites.
H. Dang, J. Weisz, and P. K. Allen, “Blind grasping: Stable robotic grasping using tactile feedback and hand kinematics,” in 2011 ieee international conference on robotics and automation . IEEE, 2011, pp. 5917–5922
2011
Earlier work this paper cites.
P. Mittendorfer, E. Yoshida, T. Moulard, and G. Cheng, “A general tactile approach for grasping unknown objects with a humanoid robot,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 4747–4752
2013
Earlier work this paper cites.
S. Dragiev, M. Toussaint, and M. Gienger, “Uncertainty aware grasping and tactile exploration,” in 2013 IEEE International conference on robotics and automation . IEEE, 2013, pp. 113–119
2013
Earlier work this paper cites.
V. Kumar, Y. Tassa, T. Erez, and E. Todorov, “Real-time behaviour synthesis for dynamic hand-manipulation,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 6808–6815
2014
Earlier work this paper cites.
Y. Bai and C. K. Liu, “Dexterous manipulation using both palm and fingers,” in 2014 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2014, pp. 1560–1565
2014
Earlier work this paper cites.
Y. Chebotar, O. Kroemer, and J. Peters, “Learning robot tactile sensing for object manipulation,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2014, pp. 3368–3375
2014
Earlier work this paper cites.
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” in 2015 international conference on advanced robotics (ICAR) . IEEE, 2015, pp. 510–517
2015
Earlier work this paper cites.
H. Liu, Y. Wu, F. Sun, and D. Guo, “Recent progress on tactile object recognition,” International Journal of Advanced Robotic Systems , vol. 14, no. 4, p. 1729881417717056, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
W. Shaw-Cortez, D. Oetomo, C. Manzie, and P. Choong, “Tactile-based blind grasping: A discrete-time object manipulation controller for robotic hands,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 1064–1071, 2018
2018
Cited alongside, same era.
O. M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray et al. , “Learning dexterous in-hand manipulation,” The International Journal of Robotics Research , vol. 39, no. 1, pp. 3–20, 2020
2020
Cited alongside, same era.
N. Chavan-Dafle and A. Rodriguez, “Sampling-based planning of in-hand manipulation with external pushes,” in Robotics Research: The 18th International Symposium ISRR . Springer, 2020, pp. 523–539
2020
Cited alongside, same era.
Q. Li, O. Kroemer, Z. Su, F. F. Veiga, M. Kaboli, and H. J. Ritter, “A review of tactile information: Perception and action through touch,” IEEE Transactions on Robotics , vol. 36, no. 6, pp. 1619–1634, 2020
2020
Y. Qin, Y.-H. Wu, S. Liu, H. Jiang, R. Yang, Y. Fu, and X. Wang, “Dexmv: Imitation learning for dexterous manipulation from human videos,” in European Conference on Computer Vision . Springer, 2022, pp. 570–587
2022
Later among the works it cites.
T. Schneider, B. Belousov, G. Chalvatzaki, D. Romeres, D. K. Jha, and J. Peters, “Active exploration for robotic manipulation,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 9355–9362
2022
Later among the works it cites.
P. K. Murali, A. Dutta, M. Gentner, E. Burdet, R. Dahiya, and M. Kaboli, “Active visuo-tactile interactive robotic perception for accurate object pose estimation in dense clutter,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4686–4693, 2022
2022
Later among the works it cites.
Z.-H. Yin, B. Huang, Y. Qin, Q. Chen, and X. Wang, “Rotating without seeing: Towards in-hand dexterity through touch,” Robotics: Science and Systems , 2023
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
A. R. Sobinov and S. J. Bensmaia, “The neural mechanisms of manual dexterity,” Nature Reviews Neuroscience , vol. 22, no. 12, pp. 741–757, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
A. Bhatt, A. Sieler, S. Puhlmann, and O. Brock, “Surprisingly Robust In-Hand Manipulation: An Empirical Study,” in Proceedings of Robotics: Science and Systems , Virtual, July 2021
2021
Cited alongside, same era.
K.-W. Lee, D.-K. Ko, and S.-C. Lim, “Toward vision-based high sampling interaction force estimation with master position and orientation for teleoperation,” IEEE Robotics and Automation Letters , vol. 6, no. 4, pp. 6640–6646, 2021
2021
Cited alongside, same era.
P. Lynch, M. F. Cullinan, and C. McGinn, “Adaptive grasping of moving objects through tactile sensing,” Sensors , vol. 21, no. 24, p. 8339, 2021
2021
Cited alongside, same era.
A. S. Morgan, K. Hang, B. Wen, K. Bekris, and A. M. Dollar, “Complex in-hand manipulation via compliance-enabled finger gaiting and multi-modal planning,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4821–4828, 2022
2022
Cited alongside, same era.
T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” in Conference on Robot Learning . PMLR, 2022, pp. 297–307
2022
Cited alongside, same era.
A. Handa, A. Allshire, V. Makoviychuk, A. Petrenko, R. Singh, J. Liu, D. Makoviichuk, K. Van Wyk, A. Zhurkevich, B. Sundaralingam et al. , “Dextreme: Transfer of agile in-hand manipulation from simulation to reality,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5977–5984
2023
Later among the works it cites.
Y. Qin, B. Huang, Z.-H. Yin, H. Su, and X. Wang, “Dexpoint: Generalizable point cloud reinforcement learning for sim-to-real dexterous manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 594–605
2023
Later among the works it cites.
T. Chen, M. Tippur, S. Wu, V. Kumar, E. Adelson, and P. Agrawal, “Visual dexterity: In-hand reorientation of novel and complex object shapes,” Science Robotics , vol. 8, no. 84, p. eadc9244, 2023
2023
Later among the works it cites.
D.-K. Ko, K.-W. Lee, D. H. Lee, and S.-C. Lim, “Vision-based interaction force estimation for robot grip motion without tactile/force sensor,” Expert Systems with Applications , vol. 211, p. 118441, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
P. K. Murali, B. Porr, and M. Kaboli, “Touch if it’s transparent! actor: Active tactile-based category-level transparent object reconstruction,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 10 792–10 799
2023
Later among the works it cites.
A. Petrenko, A. Allshire, G. State, A. Handa, and V. Makoviychuk, “Dexpbt: Scaling up dexterous manipulation for hand-arm systems with population based training,” in RSS , 2023
2023
Later among the works it cites.