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Zero-shot generalization across various robots, tasks and environments remains a significant challenge in robotic manipulation.
T. Winograd, “Procedures as a representation for data in a computer program for understanding natural language,” 1971
1971
Earlier work this paper cites.
O. Khatib, “A unified approach for motion and force control of robot manipulators: The operational space formulation,” IEEE Journal on Robotics and Automation , vol. 3, no. 1, pp. 43–53, 1987
1987
Earlier work this paper cites.
E. Rohmer, S. P. Singh, and M. Freese, “V-rep: A versatile and scalable robot simulation framework,” in 2013 IEEE/RSJ international conference on intelligent robots and systems . IEEE, 2013, pp. 1321–1326
2013
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
2019
Earlier work this paper cites.
S. James, Z. Ma, D. Rovick Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters , 2020
2020
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
S. Bahl, R. Mendonca, L. Chen, U. Jain, and D. Pathak, “Affordances from human videos as a versatile representation for robotics,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 13 778–13 790
2023
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in Conference on Robot Learning . PMLR, 2023, pp. 785–799
2023
Earlier work this paper cites.
A. Brohan, Y. Chebotar, C. Finn, K. Hausman, A. Herzog, D. Ho, J. Ibarz, A. Irpan, E. Jang, R. Julian, et al. , “Do as i can, not as i say: Grounding language in robotic affordances,” in Conference on robot learning . PMLR, 2023, pp. 287–318
2023
Cited alongside, same era.
D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, Wahid, et al. , “Palm-e: An embodied multimodal language model,” in International Conference on Machine Learning . PMLR, 2023, pp. 8469–8488
2023
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,” in Conference on Robot Learning . PMLR, 2023, pp. 1769–1782
2023
Cited alongside, same era.
I. Singh, V. Blukis, A. Mousavian, A. Goyal, D. Xu, J. Tremblay, D. Fox, J. Thomason, and A. Garg, “Progprompt: Generating situated robot task plans using large language models,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 523–11 530
2023
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
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2023
Cited alongside, same era.
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,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 9493–9500
2023
Cited alongside, same era.
2023
Cited alongside, same era.
W. Huang, C. Wang, R. Zhang, Y. Li, J. Wu, and L. Fei-Fei, “Voxposer: Composable 3d value maps for robotic manipulation with language models,” in Conference on Robot Learning . PMLR, 2023, pp. 540–562
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H.-S. Fang, C. Wang, H. Fang, M. Gou, J. Liu, H. Yan, W. Liu, Y. Xie, and C. Lu, “Anygrasp: Robust and efficient grasp perception in spatial and temporal domains,” IEEE Transactions on Robotics , 2023
2023
Cited alongside, same era.
X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 11 975–11 986
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Zhang, C. Bai, H. He, Z. Wang, B. Zhao, X. Li, and X. Li, “SAM-E: leveraging visual foundation model with sequence imitation for embodied manipulation,” in International Conference on Machine Learning , 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
Later among the works it cites.
H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 26 296–26 306
2024
Later among the works it cites.
OpenAI, “Hello gpt-4o,” May 2024. [Online]. Available: https://openai.com/index/hello-gpt-4o/
2024
Later among the works it cites.
B. Liu, Y. Zhu, C. Gao, Y. Feng, Q. Liu, Y. Zhu, and P. Stone, “Libero: Benchmarking knowledge transfer for lifelong robot learning,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Later among the works it cites.