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Vision-Language-Action Models (VLAs) have demonstrated remarkable generalization capabilities in real-world experiments.
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R. Agarwal, M. Schwarzer, P. S. Castro, A. C. Courville, and M. Bellemare, “Reincarnating reinforcement learning: Reusing prior computation to accelerate progress,” in Advances in Neural Information Processing Systems , 2022
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S. Huang, R. F. J. Dossa, C. Ye, et al. , “CleanRL: High-quality single-file implementations of deep reinforcement learning algorithms,” Journal of Machine Learning Research , vol. 23, no. 274, 2022
2022
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A. Brohan, N. Brown, J. Carbajal, et al. , “RT-1: Robotics transformer for real-world control at scale,” in Proc. of Robotics: Science and Systems (RSS) , 2023
2023
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A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, and K. Choromanski, “RT-2: Vision-language-action models transfer Web knowledge to robotic control,” in Proc. of the Conf. on Robot Learning (CoRL) , 2023
2023
Cited alongside, same era.
D. Driess, F. Xia, M. S. M. Sajjadi, et al. , “PaLM-e: An embodied multimodal language model,” in Proc. of the Int. Conf. on Machine Learning (ICML) , 2023
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Y. Jiang, A. Gupta, Z. Zhang, et al. , “VIMA: Robot manipulation with multimodal prompts,” in Proc. of the Int. Conf. on Machine Learning (ICML) , 2023
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P. J. Ball, L. Smith, I. Kostrikov, and S. Levine, “Efficient online reinforcement learning with offline data,” in Proc. of the Int. Conf. on Machine Learning (ICML) , 2023
2023
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2024
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R. Doshi, H. R. Walke, O. Mees, S. Dasari, and S. Levine, “Scaling cross-embodied learning: One policy for manipulation, navigation, locomotion and aviation,” in Proc. of the Conf. on Robot Learning (CoRL) , 2024
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G. Spigler, “Proximal policy distillation,” https://arxiv.org/abs/2407.15134 , 2024
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S. Tao, F. Xiang, A. Shukla, et al. , “ManiSkill3: GPU parallelized robotics simulation and rendering for generalizable embodied AI,” in Proc. of Robotics: Science and Systems (RSS) , 2025
2025
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