Fetching the paper…
Reading the bibliography…
Generating human-like behavior on robots is a great challenge especially in dexterous manipulation tasks with robotic hands.
R. A. Bradley and M. E. Terry, “Rank analysis of incomplete block designs: I. the method of paired comparisons,” Biometrika , vol. 39, no. 3/4, pp. 324–345, 1952
1952
Earlier work this paper cites.
L. Van der Maaten and G. Hinton, “Visualizing data using t-sne.” Journal of machine learning research , vol. 9, no. 11, 2008
2008
Earlier work this paper cites.
R. Akrour, M. Schoenauer, and M. Sebag, “Preference-based policy learning,” in Joint European Conference on Machine Learning and Knowledge Discovery in Databases . Springer, 2011, pp. 12–27
2011
Earlier work this paper cites.
M. Mori, K. F. MacDorman, and N. Kageki, “The uncanny valley [from the field],” IEEE Robotics & automation magazine , vol. 19, no. 2, pp. 98–100, 2012
2012
Earlier work this paper cites.
——, “April: Active preference learning-based reinforcement learning,” in Joint European conference on machine learning and knowledge discovery in databases . Springer, 2012, pp. 116–131
2012
Earlier work this paper cites.
S. Griffith, K. Subramanian, J. Scholz, C. L. Isbell, and A. L. Thomaz, “Policy shaping: Integrating human feedback with reinforcement learning,” Advances in neural information processing systems , vol. 26, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
C. Finn, S. Levine, and P. Abbeel, “Guided cost learning: Deep inverse optimal control via policy optimization,” in International conference on machine learning . PMLR, 2016, pp. 49–58
2016
Earlier work this paper cites.
M. Guo, D. V. Gealy, J. Liang, J. Mahler, A. Goncalves, S. McKinley, J. A. Ojea, and K. Goldberg, “Design of parallel-jaw gripper tip surfaces for robust grasping,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 2831–2838
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
D. Sadigh, A. D. Dragan, S. Sastry, and S. A. Seshia, Active preference-based learning of reward functions , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
B. Ibarz, J. Leike, T. Pohlen, G. Irving, S. Legg, and D. Amodei, “Reward learning from human preferences and demonstrations in atari,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
O. Vinyals, I. Babuschkin, W. M. Czarnecki, M. Mathieu, A. Dudzik, J. Chung, D. H. Choi, R. Powell, T. Ewalds, P. Georgiev et al. , “Grandmaster level in starcraft ii using multi-agent reinforcement learning,” Nature , vol. 575, no. 7782, pp. 350–354, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Christen, S. Stevšić, and O. Hilliges, “Guided deep reinforcement learning of control policies for dexterous human-robot interaction,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 2161–2167
2019
E. Bıyık, D. P. Losey, M. Palan, N. C. Landolfi, G. Shevchuk, and D. Sadigh, “Learning reward functions from diverse sources of human feedback: Optimally integrating demonstrations and preferences,” The International Journal of Robotics Research , vol. 41, no. 1, pp. 45–67, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Y. Jiang, T. Van Wouwe, F. De Groote, and C. K. Liu, “Synthesis of biologically realistic human motion using joint torque actuation,” ACM Transactions On Graphics (TOG) , vol. 38, no. 4, pp. 1–12, 2019
2019
Cited alongside, same era.
N. Stiennon, L. Ouyang, J. Wu, D. Ziegler, R. Lowe, C. Voss, A. Radford, D. Amodei, and P. F. Christiano, “Learning to summarize with human feedback,” Advances in Neural Information Processing Systems , vol. 33, pp. 3008–3021, 2020
2020
Cited alongside, same era.
J. Ibarz, J. Tan, C. Finn, M. Kalakrishnan, P. Pastor, and S. Levine, “How to train your robot with deep reinforcement learning: lessons we have learned,” The International Journal of Robotics Research , vol. 40, no. 4-5, pp. 698–721, 2021
2021
Cited alongside, same era.
A. Ren, S. Veer, and A. Majumdar, “Generalization guarantees for imitation learning,” in Conference on Robot Learning . PMLR, 2021, pp. 1426–1442
2021
Cited alongside, same era.
2021
Cited alongside, same era.
T. Zhu, R. Wu, X. Lin, and Y. Sun, “Toward human-like grasp: Dexterous grasping via semantic representation of object-hand,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 15 741–15 751
2021
Cited alongside, same era.
Y.-W. Chao, W. Yang, Y. Xiang, P. Molchanov, A. Handa, J. Tremblay, Y. S. Narang, K. Van Wyk, U. Iqbal, S. Birchfield et al. , “Dexycb: A benchmark for capturing hand grasping of objects,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 9044–9053
2021
Cited alongside, same era.
2022
Cited alongside, same era.
Later among the works it cites.
P. Mandikal and K. Grauman, “Dexvip: Learning dexterous grasping with human hand pose priors from video,” in Conference on Robot Learning . PMLR, 2022, pp. 651–661
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Du, P. Weinzaepfel, V. Lepetit, and R. Brégier, “Multi-finger grasping like humans,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 1564–1570
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
L. Sievers, J. Pitz, and B. Bäuml, “Learning purely tactile in-hand manipulation with a torque-controlled hand,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 2745–2751
2022
Later among the works it cites.
2022
Later among the works it cites.
P. R. Lopez, J.-H. Oh, J. G. Jeong, H. Jung, J. H. Lee, I. E. Jaramillo, C. Chola, W. H. Lee, and T.-S. Kim, “Dexterous object manipulation with an anthropomorphic robot hand via natural hand pose transformer and deep reinforcement learning,” Applied Sciences , vol. 13, no. 1, p. 379, 2023
2023
Closest in time.
H. Qi, A. Kumar, R. Calandra, Y. Ma, and J. Malik, “In-hand object rotation via rapid motor adaptation,” in Conference on Robot Learning . PMLR, 2023, pp. 1722–1732
2023
Closest in time.
Y. Xu, W. Wan, J. Zhang, H. Liu, Z. Shan, H. Shen, R. Wang, H. Geng, Y. Weng, J. Chen et al. , “Unidexgrasp: Universal robotic dexterous grasping via learning diverse proposal generation and goal-conditioned policy,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 4737–4746
2023
Closest in time.