Fetching the paper…
Reading the bibliography…
Reinforcement Learning (RL) algorithms can in principle acquire complex robotic skills by learning from large amounts of data in the real world, collected via trial and error.
1906
Earlier work this paper cites.
1906
Earlier work this paper cites.
1908
Earlier work this paper cites.
V. R. Konda and J. N. Tsitsiklis, “Actor-critic algorithms,” in Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29 - December 4, 1999] , S. A. Solla, T. K. Leen, and K. Müller, Eds. The MIT Press, 1999, pp. 1008–1014. [Online]. Available: http://papers.nips.cc/paper/1786-actor-critic-algorithms
1999
Earlier work this paper cites.
A. M. Okamura, N. Smaby, and M. R. Cutkosky, “An overview of dexterous manipulation,” in Robotics and Automation, 2000. Proceedings. ICRA’00. IEEE International Conference on , vol. 1. IEEE, 2000, pp. 255–262
2000
Earlier work this paper cites.
2001
Earlier work this paper cites.
2003
Earlier work this paper cites.
K. Yamane, J. J. Kuffner, and J. K. Hodgins, “Synthesizing animations of human manipulation tasks,” in ACM SIGGRAPH 2004 Papers . CRC press, 2004, pp. 532–539
2004
Earlier work this paper cites.
N. Furukawa, A. Namiki, S. Taku, and M. Ishikawa, “Dynamic regrasping using a high-speed multifingered hand and a high-speed vision system,” in Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006. IEEE, 2006, pp. 181–187
2006
Earlier work this paper cites.
T. Baier-Löwenstein and J. Zhang, “Learning to grasp everyday objects using reinforcement-learning with automatic value cut-off,” in 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 29 - November 2, 2007, Sheraton Hotel and Marina, San Diego, California, USA . IEEE, 2007, pp. 1551–1556. [Online]. Available: https://doi.org/10.1109/IROS.2007.4399053
2007
Earlier work this paper cites.
I. Mordatch, Z. Popović, and E. Todorov, “Contact-invariant optimization for hand manipulation,” in Proceedings of the ACM SIGGRAPH/Eurographics symposium on computer animation . Eurographics Association, 2012
2012
Earlier work this paper cites.
Y. Bai and C. K. Liu, “Dexterous manipulation using both palm and fingers,” in ICRA 2014 . IEEE, 2014
2014
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 2014, Hong Kong, China, May 31 - June 7, 2014 , 2014, pp. 6808–6815. [Online]. Available: https://doi.org/10.1109/ICRA.2014.6907864
2014
Earlier work this paper cites.
M. P. Deisenroth, P. Englert, J. Peters, and D. Fox, “Multi-task policy search for robotics,” in 2014 IEEE International Conference on Robotics and Automation, ICRA 2014, Hong Kong, China, May 31 - June 7, 2014 . IEEE, 2014, pp. 3876–3881. [Online]. Available: https://doi.org/10.1109/ICRA.2014.6907421
2014
Earlier work this paper cites.
S. Levine, N. Wagener, and P. Abbeel, “Learning contact-rich manipulation skills with guided policy search,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015
2015
Earlier work this paper cites.
H. van Hoof, T. Hermans, G. Neumann, and J. Peters, “Learning robot in-hand manipulation with tactile features,” in Humanoid Robots (Humanoids) . IEEE, 2015
2015
Earlier work this paper cites.
H. van Hoof, T. Hermans, G. Neumann, and J. Peters, “Learning robot in-hand manipulation with tactile features,” in 15th IEEE-RAS International Conference on Humanoid Robots, Humanoids 2015, Seoul, South Korea, November 3-5, 2015 . IEEE, 2015, pp. 121–127. [Online]. Available: https://doi.org/10.1109/HUMANOIDS.2015.7363524
2015
Earlier work this paper cites.
W. Han, S. Levine, and P. Abbeel, “Learning compound multi-step controllers under unknown dynamics,” in 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2015, pp. 6435–6442
2015
Earlier work this paper cites.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 3406–3413
2016
Earlier work this paper cites.
V. Kumar, E. Todorov, and S. Levine, “Optimal control with learned local models: Application to dexterous manipulation,” in 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, Stockholm, Sweden, May 16-21, 2016 , D. Kragic, A. Bicchi, and A. D. Luca, Eds. IEEE, 2016, pp. 378–383. [Online]. Available: https://doi.org/10.1109/ICRA.2016.7487156
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
R. Calandra, A. Seyfarth, J. Peters, and M. P. Deisenroth, “Bayesian optimization for learning gaits under uncertainty - an experimental comparison on a dynamic bipedal walker,” Ann. Math. Artif. Intell. , vol. 76, no. 1-2, pp. 5–23, 2016. [Online]. Available: https://doi.org/10.1007/s10472-015-9463-9
2016
Cited alongside, same era.
2018
Later among the works it cites.
A. Nair, V. Pong, M. Dalal, S. Bahl, S. Lin, and S. Levine, “Visual reinforcement learning with imagined goals,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada , S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., 2018, pp. 9209–9220. [Online]. Available: http://papers.nips.cc/paper/8132-visual-reinforcement-learning-with-imagined-goals
2018
Later among the works it cites.
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
A. Gupta, C. Eppner, S. Levine, and P. Abbeel, “Learning dexterous manipulation for a soft robotic hand from human demonstrations,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, Daejeon, South Korea, October 9-14, 2016 , 2016, pp. 3786–3793. [Online]. Available: https://doi.org/10.1109/IROS.2016.7759557
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
C. Choi, W. Schwarting, J. DelPreto, and D. Rus, “Learning object grasping for soft robot hands,” IEEE Robotics and Automation Letters , vol. 3, no. 3, pp. 2370–2377, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
R. S. Sutton and A. G. Barto, Reinforcement learning: An introduction , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
H. Zhu, A. Gupta, A. Rajeswaran, S. Levine, and V. Kumar, “Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3651–3657
2019
Later among the works it cites.
T. Yu, D. Quillen, Z. He, R. Julian, K. Hausman, C. Finn, and S. Levine, “Meta-world: A benchmark and evaluation for multi-task and meta reinforcement learning,” in 3rd Annual Conference on Robot Learning, CoRL 2019, Osaka, Japan, October 30 - November 1, 2019, Proceedings , ser. Proceedings of Machine Learning Research, L. P. Kaelbling, D. Kragic, and K. Sugiura, Eds., vol. 100. PMLR, 2019, pp. 1094–1100. [Online]. Available: http://proceedings.mlr.press/v100/yu20a.html
2019
Later among the works it cites.
S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark and learning environment,” 2019
2019
Later among the works it cites.
B. Wu, I. Akinola, J. Varley, and P. Allen, “Mat: Multi-fingered adaptive tactile grasping via deep reinforcement learning,” 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
Y. Burda, H. Edwards, A. J. Storkey, and O. Klimov, “Exploration by random network distillation,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=H1lJJnR5Ym
2019
Later among the works it cites.
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
Later among the works it cites.
A. Nagabandi, K. Konolige, S. Levine, and V. Kumar, “Deep dynamics models for learning dexterous manipulation,” in Conference on Robot Learning , 2020, pp. 1101–1112
2020
Later among the works it cites.
2020
Later among the works it cites.
K. Ploeger, M. Lutter, and J. Peters, “High acceleration reinforcement learning for real-world juggling with binary rewards,” 2020
2020
Later among the works it cites.
M. Ahn, H. Zhu, K. Hartikainen, H. Ponte, A. Gupta, S. Levine, and V. Kumar, “Robel: Robotics benchmarks for learning with low-cost robots,” in Conference on Robot Learning . PMLR, 2020, pp. 1300–1313
2020
Later among the works it cites.
S. Ha, P. Xu, Z. Tan, S. Levine, and J. Tan, “Learning to walk in the real world with minimal human effort,” 2020
2020
Later among the works it cites.
X. B. Peng, E. Coumans, T. Zhang, T.-W. Lee, J. Tan, and S. Levine, “Learning agile robotic locomotion skills by imitating animals,” 2020
2020
Later among the works it cites.
P. Mandikal and K. Grauman, “Dexterous robotic grasping with object-centric visual affordances,” 2020
2020
Later among the works it cites.