On-Robot Reinforcement Learning with Goal-Contrastive Rewards

S. Russell, “Learning agents for uncertain environments (extended abstract),” in Proceedings of the Eleventh Annual Conference on Computational Learning Theory, COLT 1998, Madison, Wisconsin, USA, July 24-26, 1998 , P. L. Bartlett and Y. Mansour, Eds. ACM, 1998, pp. 101–103

1998

Earlier work this paper cites.

J. Randløv and P. Alstrøm, “Learning to drive a bicycle using reinforcement learning and shaping,” in Proceedings of the Fifteenth International Conference on Machine Learning (ICML 1998), Madison, Wisconsin, USA, July 24-27, 1998 , J. W. Shavlik, Ed. Morgan Kaufmann, 1998, pp. 463–471

1998

Earlier work this paper cites.

A. Y. Ng, D. Harada, and S. Russell, “Policy invariance under reward transformations: Theory and application to reward shaping,” in Proceedings of the Sixteenth International Conference on Machine Learning (ICML 1999), Bled, Slovenia, June 27 - 30, 1999 , I. Bratko and S. Dzeroski, Eds. Morgan Kaufmann, 1999, pp. 278–287

1999

Earlier work this paper cites.

A. Y. Ng and S. Russell, “Algorithms for inverse reinforcement learning,” in Proceedings of the Seventeenth International Conference on Machine Learning (ICML 2000), Stanford University, Stanford, CA, USA, June 29 - July 2, 2000 , P. Langley, Ed. Morgan Kaufmann, 2000, pp. 663–670

2000

Earlier work this paper cites.

P. Abbeel and A. Y. Ng, “Apprenticeship learning via inverse reinforcement learning,” in Machine Learning, Proceedings of the Twenty-first International Conference (ICML 2004), Banff, Alberta, Canada, July 4-8, 2004 , ser. ACM International Conference Proceeding Series, C. E. Brodley, Ed., vol. 69. ACM, 2004

2004

Earlier work this paper cites.

B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning,” in Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008 , D. Fox and C. P. Gomes, Eds. AAAI Press, 2008, pp. 1433–1438

2008

Earlier work this paper cites.

S. Levine, Z. Popovic, and V. Koltun, “Nonlinear inverse reinforcement learning with gaussian processes,” in Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, Granada, Spain , J. Shawe-Taylor, R. S. Zemel, P. L. Bartlett, F. C. N. Pereira, and K. Q. Weinberger, Eds., 2011, pp. 19–27

2011

Earlier work this paper cites.

M. Wulfmeier, P. Ondruska, and I. Posner, “Maximum entropy deep inverse reinforcement learning,” CoRR , vol. abs/1507.04888, 2015

Original

2015

Earlier work this paper cites.

C. Finn, S. Levine, and P. Abbeel, “Guided cost learning: Deep inverse optimal control via policy optimization,” in Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 , ser. JMLR Workshop and Conference Proceedings, M. Balcan and K. Q. Weinberger, Eds., vol. 48. JMLR.org, 2016, pp. 49–58

2016

Earlier work this paper cites.

J. Ho and S. Ermon, “Generative adversarial imitation learning,” in Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain , D. D. Lee, M. Sugiyama, U. von Luxburg, I. Guyon, and R. Garnett, Eds., 2016, pp. 4565–4573

2016

Earlier work this paper cites.

P. Sermanet, K. Xu, and S. Levine, “Unsupervised perceptual rewards for imitation learning,” in Robotics: Science and Systems XIII, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA, July 12-16, 2017 , N. M. Amato, S. S. Srinivasa, N. Ayanian, and S. Kuindersma, Eds., 2017

2017

Earlier work this paper cites.

J. Fu, K. Luo, and S. Levine, “Learning robust rewards with adversarial inverse reinforcement learning,” CoRR , vol. abs/1710.11248, 2017

Original

2017

Earlier work this paper cites.

P. Sermanet, C. Lynch, Y. Chebotar, J. Hsu, E. Jang, S. Schaal, and S. Levine, “Time-contrastive networks: Self-supervised learning from video,” in 2018 IEEE International Conference on Robotics and Automation, ICRA 2018, Brisbane, Australia, May 21-25, 2018 . IEEE, 2018, pp. 1134–1141

2018

Earlier work this paper cites.

D. Damen, H. Doughty, G. M. Farinella, S. Fidler, A. Furnari, E. Kazakos, D. Moltisanti, J. Munro, T. Perrett, W. Price, and M. Wray, “Scaling egocentric vision: The epic-kitchens dataset,” in European Conference on Computer Vision (ECCV) , 2018

2018

Earlier work this paper cites.

J. Fu, A. Singh, D. Ghosh, L. Yang, and S. Levine, “Variational inverse control with events: A general framework for data-driven reward definition,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montréal, Canada , S. Bengio, H. M. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, Eds., 2018, pp. 8547–8556

2018

Earlier work this paper cites.

K. Fang, T. Wu, D. Yang, S. Savarese, and J. J. Lim, “Demo2vec: Reasoning object affordances from online videos,” in 2018 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2018, Salt Lake City, UT, USA, June 18-22, 2018 . Computer Vision Foundation / IEEE Computer Society, 2018, pp. 2139–2147

2018

Earlier work this paper cites.

R. S. Sutton and A. G. Barto, Reinforcement Learning: An Introduction . Cambridge, MA, USA: A Bradford Book, 2018

2018

Earlier work this paper cites.

A. van den Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” CoRR , vol. abs/1807.03748, 2018

Original

2018

Earlier work this paper cites.

A. Singh, L. Yang, C. Finn, and S. Levine, “End-to-end robotic reinforcement learning without reward engineering,” in Robotics: Science and Systems XV, University of Freiburg, Freiburg im Breisgau, Germany, June 22-26, 2019 , A. Bicchi, H. Kress-Gazit, and S. Hutchinson, Eds., 2019

2019

Earlier work this paper cites.

O. Nachum, B. Dai, I. Kostrikov, Y. Chow, L. Li, and D. Schuurmans, “Algaedice: Policy gradient from arbitrary experience,” CoRR , vol. abs/1912.02074, 2019

Original

2019

Earlier work this paper 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

2019

Earlier work this paper cites.

S. Reddy, A. D. Dragan, and S. Levine, “SQIL: imitation learning via reinforcement learning with sparse rewards,” in 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020 . OpenReview.net, 2020

2020

Earlier work this paper cites.

L. M. Smith, N. Dhawan, M. Zhang, P. Abbeel, and S. Levine, “AVID: learning multi-stage tasks via pixel-level translation of human videos,” in Robotics: Science and Systems XVI, Virtual Event / Corvalis, Oregon, USA, July 12-16, 2020 , M. Toussaint, A. Bicchi, and T. Hermans, Eds., 2020

2020

Earlier work this paper cites.

K. Schmeckpeper, O. Rybkin, K. Daniilidis, S. Levine, and C. Finn, “Reinforcement learning with videos: Combining offline observations with interaction,” arXiv preprint arXiv:2011.06507 , 2020

Original

2020

Earlier work this paper cites.

K. Schmeckpeper, A. Xie, O. Rybkin, S. Tian, K. Daniilidis, S. Levine, and C. Finn, “Learning predictive models from observation and interaction,” in European Conference on Computer Vision . Springer, 2020, pp. 708–725

2020

Cited alongside, same era.

A. Mandlekar, D. Xu, J. Wong, S. Nasiriany, C. Wang, R. Kulkarni, L. Fei-Fei, S. Savarese, Y. Zhu, and R. Martín-Martín, “What matters in learning from offline human demonstrations for robot manipulation,” in Conference on Robot Learning, 8-11 November 2021, London, UK , ser. Proceedings of Machine Learning Research, A. Faust, D. Hsu, and G. Neumann, Eds., vol. 164. PMLR, 2021, pp. 1678–1690

2021

Cited alongside, same era.

H. Xiong, Q. Li, Y. Chen, H. Bharadhwaj, S. Sinha, and A. Garg, “Learning by watching: Physical imitation of manipulation skills from human videos,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021, Prague, Czech Republic, September 27 - Oct. 1, 2021 . IEEE, 2021, pp. 7827–7834

2021

Cited alongside, same era.

D. Ghosh, C. A. Bhateja, and S. Levine, “Reinforcement learning from passive data via latent intentions,” in International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA , ser. Proceedings of Machine Learning Research, A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, and J. Scarlett, Eds., vol. 202. PMLR, 2023, pp. 11 321–11 339

2023

Later among the works it cites.

C. Bhateja, D. Guo, D. Ghosh, A. Singh, M. Tomar, Q. Vuong, Y. Chebotar, S. Levine, and A. Kumar, “Robotic offline RL from internet videos via value-function pre-training,” CoRR , vol. abs/2309.13041, 2023

Original

2023

Later among the works it cites.

O. X. Collaboration, A. Padalkar, A. Pooley, A. Jain, A. Bewley, A. Herzog, A. Irpan, A. Khazatsky, A. Raj, A. Singh, A. Brohan, A. Raffin, A. Wahid, B. Burgess-Limerick, B. Kim, B. Schölkopf, B. Ichter, C. Lu, C. Xu, C. Finn, C. Xu, C. Chi, C. Huang, C. Chan, C. Pan, C. Fu, C. Devin, D. Driess, D. Pathak, D. Shah, D. Büchler, D. Kalashnikov, D. Sadigh, E. Johns, F. Ceola, F. Xia, F. Stulp, G. Zhou, G. S. Sukhatme, G. Salhotra, G. Yan, G. Schiavi, G. Kahn, H. Su, H. Fang, H. Shi, H. B. Amor, H. I. Christensen, H. Furuta, H. Walke, H. Fang, I. Mordatch, I. Radosavovic, and et al., “Open x-embodiment: Robotic learning datasets and RT-X models,” CoRR , vol. abs/2310.08864, 2023

Original

2023

Later among the works it cites.