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We demonstrate experimental results with LLMs that address robotics task planning problems.
M. Schilling and A. Melnik, “An approach to hierarchical deep reinforcement learning for a decentralized walking control architecture,” in Biologically Inspired Cognitive Architectures 2018: Proceedings of the Ninth Annual Meeting of the BICA Society . Springer, 2019, pp. 272–282
2019
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N. Bach, A. Melnik, M. Schilling, T. Korthals, and H. Ritter, “Learn to move through a combination of policy gradient algorithms: Ddpg, d4pg, and td3,” in Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part II 6 . Springer, 2020, pp. 631–644
2020
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A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
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A. Melnik, L. Lach, M. Plappert, T. Korthals, R. Haschke, and H. Ritter, “Using tactile sensing to improve the sample efficiency and performance of deep deterministic policy gradients for simulated in-hand manipulation tasks,” Frontiers in Robotics and AI , vol. 8, p. 538773, 2021
2021
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M. Tsimpoukelli, J. Menick, S. Cabi, S. M. A. Eslami, O. Vinyals, and F. Hill, “Multimodal few-shot learning with frozen language models,” 2021
2021
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2022
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, F. Xia, E. Chi, Q. V. Le, D. Zhou et al. , “Chain-of-thought prompting elicits reasoning in large language models,” Advances in neural information processing systems , vol. 35, pp. 24 824–24 837, 2022
2022
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2022
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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,” 2022
2022
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2022
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2022
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E. Jang, A. Irpan, M. Khansari, D. Kappler, F. Ebert, C. Lynch, S. Levine, and C. Finn, “Bc-z: Zero-shot task generalization with robotic imitation learning,” in Conference on Robot Learning . PMLR, 2022, pp. 991–1002
2022
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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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2023
Earlier work this paper cites.
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.
Y.-J. Wang, B. Zhang, J. Chen, and K. Sreenath, “Prompt a robot to walk with large language models,” 2023
2023
Cited alongside, same era.
C. Li, J. Liang, A. Zeng, X. Chen, K. Hausman, D. Sadigh, S. Levine, L. Fei-Fei, F. Xia, and B. Ichter, “Chain of code: Reasoning with a language model-augmented code emulator,” 2023
2023
Cited alongside, same era.
J. Li, Q. Gao, M. Johnston, X. Gao, X. He, S. Shakiah et al. , “Mastering robot manipulation with multimodal prompts through pretraining and multi-task fine-tuning,” 2023
2023
Later among the works it cites.
K. Rana, A. Melnik, and N. Sünderhauf, “Contrastive language, action, and state pre-training for robot learning,” 2023
2023
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2023
Later among the works it cites.
N. H. Matthias Minderer, Alexey Gritsenko, “Scaling open-vocabulary object detection,” NeurIPS , 2023
2023
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2023
Cited alongside, same era.
T. Kwon, N. D. Palo, and E. Johns, “Language models as zero-shot trajectory generators,” 2023
2023
Cited alongside, same era.
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,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
H. Wang, G. Gonzalez-Pumariega, Y. Sharma, and S. Choudhury, “Demo2code: From summarizing demonstrations to synthesizing code via extended chain-of-thought,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
K. Lin, C. Agia, T. Migimatsu, M. Pavone, and J. Bohg, “Text2motion: from natural language instructions to feasible plans,” Autonomous Robots , vol. 47, no. 8, p. 1345–1365, Nov. 2023
2023
Cited alongside, same era.
2023
Later among the works it cites.
G. Wang, Y. Xie, Y. Jiang, A. Mandlekar, C. Xiao, Y. Zhu, L. Fan, and A. Anandkumar, “Voyager: An open-ended embodied agent with large language models,” 2023
2023
Later among the works it cites.
Y. Tang, W. Yu, J. Tan, H. Zen, A. Faust, and T. Harada, “Saytap: Language to quadrupedal locomotion,” 2023
2023
Later among the works it cites.
M. Rothgaenger, A. Melnik, and H. Ritter, “Shape complexity estimation using vae,” in Intelligent Systems Conference . Springer, 2023, pp. 35–45
2023
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2024
Closest in time.
Y. Mu, Q. Zhang, M. Hu, W. Wang, M. Ding, J. Jin, B. Wang, J. Dai, Y. Qiao, and P. Luo, “Embodiedgpt: Vision-language pre-training via embodied chain of thought,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
2024
Closest in time.
Y. Mu, J. Chen, Q. Zhang, S. Chen, Q. Yu, C. Ge, and et al., “Robocodex: Multimodal code generation for robotic behavior synthesis,” 2024
2024
Closest in time.
H. Chae, Y. Kim, S. Kim, K. T. iunn Ong, B. woo Kwak, M. Kim, S. Kim, T. Kwon, J. Chung, Y. Yu, and J. Yeo, “Language models as compilers: Simulating pseudocode execution improves algorithmic reasoning in language models,” 2024
2024
Closest in time.