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OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents

Z. Wang, S. Cai, Z. Mu, H. Lin, C. Zhang, X. Liu, Q. Li, A. Liu, X. Ma, and Y. Liang. In Proceedings of the 38th Annual Conference on Neural Information Processing Systems (NeurIPS 2024), (2024)
DOI: https://doi.org/10.48550/arXiv.2407.00114

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State of the Art Control of Atari Games Using Shallow Reinforcement Learning.Y. Liang, M. Machado, E. Talvitie, and M. Bowling. AAMAS, page 485-493. ACM, (2016)Horizon: Facebook's Open Source Applied Reinforcement Learning Platform.J. Gauci, E. Conti, Y. Liang, K. Virochsiri, Y. He, Z. Kaden, V. Narayanan, and X. Ye. CoRR, (2018)On Tractable Computation of Expected Predictions.P. Khosravi, Y. Choi, Y. Liang, A. Vergari, and G. den Broeck. NeurIPS, page 11167-11178. (2019)A Semantic Loss Function for Deep Learning with Symbolic Knowledge.J. Xu, Z. Zhang, T. Friedman, Y. Liang, and G. den Broeck. CoRR, (2017)State of the Art Control of Atari Games Using Shallow Reinforcement Learning.Y. Liang, M. Machado, E. Talvitie, and M. Bowling. CoRR, (2015)Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing.Z. Hu, Y. Liang, Y. Liu, and J. Zhang. CoRR, (2018)Learning Logistic Circuits.Y. Liang, and G. den Broeck. AAAI, page 4277-4286. AAAI Press, (2019)Inference Aided Reinforcement Learning for Incentive Mechanism Design in Crowdsourcing.Z. Hu, Y. Liang, J. Zhang, Z. Li, and Y. Liu. NeurIPS, page 5512-5522. (2018)An application of optimization method for storyline based on cluster analysis.Y. Liu, H. Lin, Y. Liang, and C. Wang. VINCI, page 24-28. ACM, (2017)LaTeS: Latent Space Distillation for Teacher-Student Driving Policy Learning.A. Zhao, T. He, Y. Liang, H. Huang, G. den Broeck, and S. Soatto. CoRR, (2019)

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OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents

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OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents