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The application of vision-language models (VLMs) has achieved impressive success in various robotics tasks.
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J.-P. Sleiman, F. Farshidian, M. V. Minniti, and M. Hutter, “A unified mpc framework for whole-body dynamic locomotion and manipulation,” IEEE Robotics and Automation Letters , vol. 6, no. 3, pp. 4688–4695, 2021
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J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International conference on machine learning . PMLR, 2022, pp. 12 888–12 900
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W. Huang, P. Abbeel, D. Pathak, and I. Mordatch, “Language models as zero-shot planners: Extracting actionable knowledge for embodied agents,” in International Conference on Machine Learning . PMLR, 2022, pp. 9118–9147
2022
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P. Mahmoudieh, D. Pathak, and T. Darrell, “Zero-shot reward specification via grounded natural language,” in International Conference on Machine Learning . PMLR, 2022, pp. 14 743–14 752
2022
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T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics , vol. 7, no. 62, p. eabk2822, 2022
2022
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A. Agrawal, S. Chen, A. Rai, and K. Sreenath, “Vision-aided dynamic quadrupedal locomotion on discrete terrain using motion libraries,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 4708–4714
2022
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G. Ji, J. Mun, H. Kim, and J. Hwangbo, “Concurrent training of a control policy and a state estimator for dynamic and robust legged locomotion,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 4630–4637, 2022
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2022
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G. B. Margolis and P. Agrawal, “Walk these ways: Tuning robot control for generalization with multiplicity of behavior,” in Conference on Robot Learning . PMLR, 2023, pp. 22–31
2023
Cited alongside, same era.
2023
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A. Chowdhery, S. Narang, J. Devlin, M. Bosma, G. Mishra, A. Roberts, P. Barham, H. W. Chung, C. Sutton, S. Gehrmann, et al. , “Palm: Scaling language modeling with pathways,” Journal of Machine Learning Research , vol. 24, no. 240, pp. 1–113, 2023
2023
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2023
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2023
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J. Wu, G. Xin, C. Qi, and Y. Xue, “Learning robust and agile legged locomotion using adversarial motion priors,” IEEE Robotics and Automation Letters , 2023
2023
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P. Wu, A. Escontrela, D. Hafner, P. Abbeel, and K. Goldberg, “Daydreamer: World models for physical robot learning,” in Conference on Robot Learning . PMLR, 2023, pp. 2226–2240
2023
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I. M. A. Nahrendra, B. Yu, and H. Myung, “Dreamwaq: Learning robust quadrupedal locomotion with implicit terrain imagination via deep reinforcement learning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 5078–5084
2023
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
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.
B. Chen, F. Xia, B. Ichter, K. Rao, K. Gopalakrishnan, M. S. Ryoo, A. Stone, and D. Kappler, “Open-vocabulary queryable scene representations for real world planning,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 11 509–11 522
2023
Cited alongside, same era.
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.
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
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2023
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2023
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2023
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2024
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2024
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I. Kapelyukh, Y. Ren, I. Alzugaray, and E. Johns, “Dream2Real: Zero-shot 3D object rearrangement with vision-language models,” in IEEE International Conference on Robotics and Automation (ICRA) , 2024
2024
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A. Lykov, M. Litvinov, M. Konenkov, R. Prochii, N. Burtsev, A. A. Abdulkarim, A. Bazhenov, V. Berman, and D. Tsetserukou, “Cognitivedog: Large multimodal model based system to translate vision and language into action of quadruped robot,” in Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction , 2024, pp. 712–716
2024
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G. B. Margolis, G. Yang, K. Paigwar, T. Chen, and P. Agrawal, “Rapid locomotion via reinforcement learning,” The International Journal of Robotics Research , vol. 43, no. 4, pp. 572–587, 2024
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H. Liu, C. Li, Y. Li, B. Li, Y. Zhang, S. Shen, and Y. J. Lee, “Llava-next: Improved reasoning, ocr, and world knowledge,” January 2024. [Online]. Available: https://llava-vl.github.io/blog/2024-01-30-llava-next/
2024
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