Fetching the paper…
Reading the bibliography…
Consider a robot tasked with tidying a desk with a meticulously constructed Lego sports car.
M. Cakmak, S. S. Srinivasa, M. K. Lee, J. Forlizzi, and S. Kiesler, “Human preferences for robot-human hand-over configurations,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2011, pp. 1986–1993
1993
Earlier work this paper cites.
N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich, “Maximum margin planning,” Pittsburgh, Pennsylvania, 2006
2006
Earlier work this paper cites.
B. D. Ziebart, A. L. Maas, J. A. Bagnell, and A. K. Dey, “Maximum entropy inverse reinforcement learning.” in Aaai , vol. 8, 2008, pp. 1433–1438
2008
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics . JMLR Workshop and Conference Proceedings, 2011, pp. 627–635
2011
Earlier work this paper cites.
R. Akrour, M. Schoenauer, and M. Sebag, “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.
D. Sadigh, A. D. Dragan, S. S. Sastry, and S. A. Seshia, “Active preference-based learning of reward functions,” in Proceedings of Robotics: Science and Systems (RSS) , July 2017
2017
Earlier work this paper cites.
J. Bohg, K. Hausman, B. Sankaran, O. Brock, D. Kragic, S. Schaal, and G. Sukhatme, “Interactive Perception: Leveraging Action in Perception and Perception in Action,” IEEE Transactions on Robotics , vol. 33, no. 6, pp. 1273–1291, Dec. 2017
2017
Earlier work this paper cites.
D. S. Brown, W. Goo, and S. Niekum, “Better-than-demonstrator imitation learning via automatically-ranked demonstrations,” Oct. 2019
2019
Earlier work this paper cites.
M. Palan, N. C. Landolfi, G. Shevchuk, and D. Sadigh, “Learning Reward Functions by Integrating Human Demonstrations and Preferences,” June 2019
2019
Earlier work this paper cites.
R. Zellers, Y. Bisk, A. Farhadi, and Y. Choi, “From Recognition to Cognition: Visual Commonsense Reasoning,” Mar. 2019
2019
Earlier work this paper cites.
J. Carpentier, G. Saurel, G. Buondonno, J. Mirabel, F. Lamiraux, O. Stasse, and N. Mansard, “The pinocchio c++ library – a fast and flexible implementation of rigid body dynamics algorithms and their analytical derivatives,” in IEEE International Symposium on System Integrations (SII) , 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al. , “Language Models are Few-Shot Learners,” 2020
2020
Earlier work this paper cites.
X. Zhou, Y. Zhang, L. Cui, and D. Huang, “Evaluating Commonsense in Pre-Trained Language Models,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, pp. 9733–9740, Apr. 2020
2020
Earlier work this paper cites.
D. M. Ziegler, N. Stiennon, J. Wu, T. B. Brown, A. Radford, D. Amodei, P. Christiano, and G. Irving, “Fine-Tuning Language Models from Human Preferences,” Jan. 2020
2020
Earlier work this paper cites.
M. Li, A. Canberk, D. P. Losey, and D. Sadigh, “Learning human objectives from sequences of physical corrections,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 2877–2883
2021
Earlier work this paper cites.
E. Biyik, 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,” 2021
2021
Earlier work this paper cites.
L. Shao, T. Migimatsu, Q. Zhang, K. Yang, and J. Bohg, “Concept2Robot: Learning manipulation concepts from instructions and human demonstrations,” The International Journal of Robotics Research , vol. 40, no. 12-14, pp. 1419–1434, Dec. 2021
2021
Cited alongside, same era.
S. Mirchandani, S. Karamcheti, and D. Sadigh, “Ella: Exploration through learned language abstraction,” Oct. 2021
2021
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Cited alongside, same era.
2022
Cited alongside, same era.
S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-maron, M. Giménez, Y. Sulsky, J. Kay, J. T. Springenberg, et al. , “A Generalist Agent,” Transactions on Machine Learning Research , Nov. 2022
2022
Later among the works it cites.
Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “VIMA: General Robot Manipulation with Multimodal Prompts,” Oct. 2022
2022
Later among the works it cites.
J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain of thought prompting elicits reasoning in large language models,” arXiv , 2022
2022
Later among the works it cites.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Jiang, J. D. Hwang, C. Bhagavatula, R. L. Bras, J. Liang, J. Dodge, K. Sakaguchi, M. Forbes, J. Borchardt, S. Gabriel, et al. , “Can Machines Learn Morality? The Delphi Experiment,” July 2022
2022
Cited alongside, same era.
Z. Jin, S. Levine, F. Gonzalez Adauto, O. Kamal, M. Sap, M. Sachan, R. Mihalcea, J. Tenenbaum, and B. Schölkopf, “When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment,” Advances in Neural Information Processing Systems , vol. 35, pp. 28 458–28 473, Dec. 2022
2022
Cited alongside, same era.
K. C. Fraser, S. Kiritchenko, and E. Balkir, “Does Moral Code Have a Moral Code? Probing Delphi’s Moral Philosophy,” May 2022
2022
Cited alongside, same era.
P. Ammanabrolu, L. Jiang, M. Sap, H. Hajishirzi, and Y. Choi, “Aligning to Social Norms and Values in Interactive Narratives,” May 2022
2022
Cited alongside, same era.
D. Hendrycks, M. Mazeika, A. Zou, S. Patel, C. Zhu, J. Navarro, D. Song, B. Li, and J. Steinhardt, “What Would Jiminy Cricket Do? Towards Agents That Behave Morally,” Feb. 2022
2022
Cited alongside, same era.
L. Fan, G. Wang, Y. Jiang, A. Mandlekar, Y. Yang, H. Zhu, A. Tang, D.-A. Huang, Y. Zhu, and A. Anandkumar, “MineDojo: Building Open-Ended Embodied Agents with Internet-Scale Knowledge,” June 2022
2022
Cited alongside, same era.
W. Huang, F. Xia, T. Xiao, H. Chan, J. Liang, P. Florence, A. Zeng, J. Tompson, I. Mordatch, Y. Chebotar, et al. , “Inner Monologue: Embodied Reasoning through Planning with Language Models,” July 2022
2022
Cited alongside, same era.
J. Yu, Y. Xu, J. Y. Koh, T. Luong, G. Baid, Z. Wang, V. Vasudevan, A. Ku, Y. Yang, B. K. Ayan, et al. , “Scaling autoregressive models for content-rich text-to-image generation,” arXiv , 2022
2022
Cited alongside, same era.
B. Zhang and H. Soh, “Large Language Models as Zero-Shot Human Models for Human-Robot Interaction,” Mar. 2023
2023
Closest in time.
D. Hendrycks, C. Burns, S. Basart, A. Critch, J. Li, D. Song, and J. Steinhardt, “Aligning AI With Shared Human Values,” Feb. 2023
2023
Closest in time.
H. Hu and D. Sadigh, “Language instructed reinforcement learning for human-ai coordination,” in 40th International Conference on Machine Learning (ICML) , 2023
2023
Closest in time.
J. Wu, R. Antonova, A. Kan, M. Lepert, A. Zeng, S. Song, J. Bohg, S. Rusinkiewicz, and T. Funkhouser, “TidyBot: Personalized Robot Assistance with Large Language Models,” May 2023
2023
Closest in time.
X. Zhao, M. Li, C. Weber, M. B. Hafez, and S. Wermter, “Chat with the Environment: Interactive Multimodal Perception using Large Language Models,” Mar. 2023
2023
Closest in time.
C. Huang, O. Mees, A. Zeng, and W. Burgard, “Visual Language Maps for Robot Navigation,” Mar. 2023
2023
Closest in time.
J. Liang, W. Huang, F. Xia, P. Xu, K. Hausman, B. Ichter, P. Florence, and A. Zeng, “Code as Policies: Language Model Programs for Embodied Control,” May 2023
2023
Closest in time.
D. Surís, S. Menon, and C. Vondrick, “ViperGPT: Visual Inference via Python Execution for Reasoning,” Mar. 2023
2023
Closest in time.
D. Driess, F. Xia, M. S. M. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu, et al. , “PaLM-E: An Embodied Multimodal Language Model,” Mar. 2023
2023
Closest in time.
2023
Closest in time.
J. Kerr, C. M. Kim, K. Goldberg, A. Kanazawa, and M. Tancik, “LERF: Language Embedded Radiance Fields,” Mar. 2023
2023
Closest in time.
M. A. Research, “Polymetis: A real-time pytorch controller manager,” https://github.com/facebookresearch/fairo/tree/main/polymetis, 2021–2023
2023
Closest in time.