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Visuomotor robot policies, increasingly pre-trained on large-scale datasets, promise significant advancements across robotics domains.
arXiv preprint arXiv:1906.08113
Xiao H, Herman M, Wagner J, Ziesche S, Etesami J and Linh TH (2019) Wasserstein adversarial imitation learning · 1906
Earlier work this paper cites.
Biometrika 39(3/4): 324–345
Bradley RA and Terry ME (1952) Rank analysis of incomplete block designs: I. the method of paired comparisons · 1952
Earlier work this paper cites.
Vestnik of the St. Petersburg University: Mathematics 13(7): 52–59
Kantorovich LV and Rubinshtein S (1958) On a space of totally additive functions · 1958
Earlier work this paper cites.
Advances in neural information processing systems 1
Pomerleau DA (1988) Alvinn: An autonomous land vehicle in a neural network · 1988
Earlier work this paper cites.
In: Proceedings of the twenty-first international conference on Machine learning . p. 1
Abbeel P and Ng AY (2004) Apprenticeship learning via inverse reinforcement learning · 2004
Earlier work this paper cites.
In: Aaai , volume 8. Chicago, IL, USA, pp. 1433–1438
Ziebart BD, Maas AL, Bagnell JA, Dey AK et al. (2008) Maximum entropy inverse reinforcement learning · 2008
Earlier work this paper cites.
In: 2009 IEEE conference on computer vision and pattern recognition . Ieee, pp. 248–255
Deng J, Dong W, Socher R, Li LJ, Li K and Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database · 2009
Earlier work this paper cites.
In: 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, pp. 1077–1084
Jamieson KG and Nowak RD (2011) Low-dimensional embedding using adaptively selected ordinal data · 2011
Earlier work this paper cites.
In: Shawe-Taylor J, Zemel R, Bartlett P, Pereira F and Weinberger K (eds.) Advances in Neural Information Processing Systems , volume 24. Curran Associates, Inc
Levine S, Popovic Z and Koltun V (2011) Nonlinear inverse reinforcement learning with gaussian processes · 2011
Earlier work this paper cites.
In: International conference on machine learning . PMLR, pp. 49–58
Finn C, Levine S and Abbeel P (2016) Guided cost learning: Deep inverse optimal control via policy optimization · 2016
Earlier work this paper cites.
In: Proceedings of the IEEE conference on computer vision and pattern recognition . pp. 770–778
He K, Zhang X, Ren S and Sun J (2016) Deep residual learning for image recognition · 2016
Earlier work this paper cites.
Robotics: Science and Systems
Sermanet P, Xu K and Levine S (2016) Unsupervised perceptual rewards for imitation learning · 2016
Earlier work this paper cites.
Advances in neural information processing systems 30
Christiano PF, Leike J, Brown T, Martic M, Legg S and Amodei D (2017) Deep reinforcement learning from human preferences · 2017
Earlier work this paper cites.
Robotics: Science and Systems
Sadigh D, Dragan AD, Sastry S and Seshia SA (2017) Active preference-based learning of reward functions · 2017
Earlier work this paper cites.
arXiv preprint arXiv:1707.06347
Schulman J, Wolski F, Dhariwal P, Radford A and Klimov O (2017) Proximal policy optimization algorithms · 2017
Earlier work this paper cites.
In: 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 6923–6930
Bullard K, Chernova S and Thomaz AL (2018) Human-driven feature selection for a robotic agent learning classification tasks from demonstration · 2018
Earlier work this paper cites.
In: International conference on machine learning . PMLR, pp. 1861–1870
Haarnoja T, Zhou A, Abbeel P and Levine S (2018) Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor · 2018
Earlier work this paper cites.
In: International conference on machine learning . PMLR, pp. 783–792
Brown D, Goo W, Nagarajan P and Niekum S (2019) Extrapolating beyond suboptimal demonstrations via inverse reinforcement learning from observations · 2019
Earlier work this paper cites.
In: 2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM) . IEEE, pp. 372–377
Luu-Duc H and Miura J (2019) An incremental feature set refinement in a programming by demonstration scenario · 2019
Earlier work this paper cites.
Foundations and Trends® in Machine Learning 11(5-6): 355–607
Peyré G, Cuturi M et al. (2019) Computational optimal transport: With applications to data science · 2019
Cited alongside, same era.
In: International conference on machine learning . PMLR, pp. 6036–6045
Sun W, Vemula A, Boots B and Bagnell D (2019) Provably efficient imitation learning from observation alone · 2019
Cited alongside, same era.
In: Proceedings of Robotics: Science and Systems (RSS)
Shao L, Migimatsu T, Zhang Q, Yang K and Bohg J (2020) Concept2Robot: Learning manipulation concepts from instructions and human demonstrations · 2020
Cited alongside, same era.
In: 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, pp. 2174–2181
Tanwani AK, Sermanet P, Yan A, Anand R, Phielipp M and Goldberg K (2020) Motion2vec: Semi-supervised representation learning from surgical videos · 2020
Cited alongside, same era.
International Conference on Learning Representations
Zhang A, McAllister R, Calandra R, Gal Y and Levine S (2020) Learning invariant representations for reinforcement learning without reconstruction · 2020
Cited alongside, same era.
In: Conference on Robot Learning . PMLR, pp. 537–546
Zakka K, Zeng A, Florence P, Tompson J, Bohg J and Dwibedi D (2022) Xirl: Cross-embodiment inverse reinforcement learning · 2022
Later among the works it cites.
International Conference on Human Robot Interaction
Bobu A, Liu Y, Shah R, Brown DS and Dragan AD (2023) Sirl: Similarity-based implicit representation learning · 2023
Later among the works it cites.
arXiv preprint arXiv:2307.15818
Brohan A, Brown N, Carbajal J, Chebotar Y, Chen X, Choromanski K, Ding T, Driess D, Dubey A, Finn C et al. (2023) Rt-2: Vision-language-action models transfer web knowledge to robotic control · 2023
Later among the works it cites.
arXiv preprint arXiv:2309.12300
Guzey I, Dai Y, Evans B, Chintala S and Pinto L (2023) See to touch: Learning tactile dexterity through visual incentives · 2023
Later among the works it cites.
In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . pp. 17853–17862
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In: Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction . pp. 216–224
Bobu A, Wiggert M, Tomlin C and Dragan AD (2021) Feature expansive reward learning: Rethinking human input · 2021
Cited alongside, same era.
Neuron 109(17): 2755–2766
Bonnen T, Yamins DL and Wagner AD (2021) When the ventral visual stream is not enough: A deep learning account of medial temporal lobe involvement in perception · 2021
Cited alongside, same era.
PLoS computational biology 17(3): e1008863
Callaway F, Rangel A and Griffiths TL (2021) Fixation patterns in simple choice reflect optimal information sampling · 2021
Cited alongside, same era.
Robotics: Science and Systems
Chen AS, Nair S and Finn C (2021) Learning generalizable robotic reward functions from” in-the-wild” human videos · 2021
Cited alongside, same era.
International Conference on Robot Learning
Dadashi R, Hussenot L, Geist M and Pietquin O (2021) Primal wasserstein imitation learning · 2021
Cited alongside, same era.
arXiv preprint arXiv:2103.02727
Katz SM, Maleki A, Bıyık E and Kochenderfer MJ (2021) Preference-based learning of reward function features · 2021
Cited alongside, same era.
arXiv preprint arXiv:2108.10470
Makoviychuk V, Wawrzyniak L, Guo Y, Lu M, Storey K, Macklin M, Hoeller D, Rudin N, Allshire A, Handa A et al. (2021) Isaac gym: High performance gpu-based physics simulation for robot learning · 2021
Cited alongside, same era.
Hu Y, Yang J, Chen L, Li K, Sima C, Zhu X, Chai S, Du S, Lin T, Wang W et al. (2023) Planning-oriented autonomous driving · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 55–66
Kumar S, Zamora J, Hansen N, Jangir R and Wang X (2023) Graph inverse reinforcement learning from diverse videos · 2023
Later among the works it cites.
arXiv preprint arXiv:2302.12192
Lee K, Liu H, Ryu M, Watkins O, Du Y, Boutilier C, Abbeel P, Ghavamzadeh M and Gu SS (2023) Aligning text-to-image models using human feedback · 2023
Later among the works it cites.
In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, pp. 7553–7560
Lu Y, Fu J, Tucker G, Pan X, Bronstein E, Roelofs R, Sapp B, White B, Faust A, Whiteson S et al. (2023) Imitation is not enough: Robustifying imitation with reinforcement learning for challenging driving scenarios · 2023
Later among the works it cites.
In: The Eleventh International Conference on Learning Representations
Luo Y, zhengyao jiang, Cohen S, Grefenstette E and Deisenroth MP (2023) Optimal transport for offline imitation learning · 2023
Later among the works it cites.
International Conference on Learning Representations
Ma YJ, Sodhani S, Jayaraman D, Bastani O, Kumar V and Zhang A (2023) Vip: Towards universal visual reward and representation via value-implicit pre-training · 2023
Later among the works it cites.
arXiv preprint arXiv:2310.08864
Padalkar A, Pooley A, Jain A, Bewley A, Herzog A, Irpan A, Khazatsky A, Rai A, Singh A, Brohan A et al. (2023) Open x-embodiment: Robotic learning datasets and rt-x models · 2023
Later among the works it cites.
In: Conference on Robot Learning . PMLR, pp. 416–426
Radosavovic I, Xiao T, James S, Abbeel P, Malik J and Darrell T (2023) Real-world robot learning with masked visual pre-training · 2023
Later among the works it cites.
In: Proceedings of the IEEE/CVF International Conference on Computer Vision . pp. 8579–8590
Seff A, Cera B, Chen D, Ng M, Zhou A, Nayakanti N, Refaat KS, Al-Rfou R and Sapp B (2023) Motionlm: Multi-agent motion forecasting as language modeling · 2023
Later among the works it cites.
arXiv preprint arXiv:2301.11990
Sucholutsky I and Griffiths TL (2023) Alignment with human representations supports robust few-shot learning · 2023
Later among the works it cites.
arXiv preprint arXiv:2310.16944
Tunstall L, Beeching E, Lambert N, Rajani N, Rasul K, Belkada Y, Huang S, von Werra L, Fourrier C, Habib N et al. (2023) Zephyr: Direct distillation of lm alignment · 2023
Later among the works it cites.
arXiv preprint arXiv:2307.12950
Yang K, Klein D, Celikyilmaz A, Peng N and Tian Y (2023) Rlcd: Reinforcement learning from contrast distillation for language model alignment · 2023
Later among the works it cites.
The International Journal of Robotics Research
Chi C, Xu Z, Feng S, Cousineau E, Du Y, Burchfiel B, Tedrake R and Song S (2024) Diffusion policy: Visuomotor policy learning via action diffusion · 2024
Closest in time.
arXiv preprint arXiv:2402.11907
Liu A, Bai H, Lu Z, Kong X, Wang S, Shan J, Cao M and Wen L (2024) Direct large language model alignment through self-rewarding contrastive prompt distillation · 2024
Closest in time.
Advances in Neural Information Processing Systems 36
Rafailov R, Sharma A, Mitchell E, Manning CD, Ermon S and Finn C (2024) Direct preference optimization: Your language model is secretly a reward model · 2024
Closest in time.