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Vision-Language-Action (VLA) models frequently encounter challenges in generalizing to real-world environments due to inherent discrepancies between observation and action spaces.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
E. Perez, F. Strub, H. De Vries, V. Dumoulin, and A. Courville, “Film: Visual reasoning with a general conditioning layer,” in Proceedings of the AAAI conference on artificial intelligence , vol. 32, no. 1, 2018
2018
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “Decoupled weight decay regularization,” in 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019 . OpenReview.net, 2019. [Online]. Available: https://openreview.net/forum?id=Bkg6RiCqY7
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , H. Larochelle, M. Ranzato, R. Hadsell, M. Balcan, and H. Lin, Eds., 2020. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/4c5bcfec8584af0d967f1ab10179ca4b-Abstract.html
2020
Earlier work this paper cites.
O. Kroemer, S. Niekum, and G. Konidaris, “A review of robot learning for manipulation: Challenges, representations, and algorithms,” Journal of machine learning research , vol. 22, no. 30, pp. 1–82, 2021
2021
Earlier work this paper cites.
J. Yamada, Y. Lee, G. Salhotra, K. Pertsch, M. Pflueger, G. Sukhatme, J. Lim, and P. Englert, “Motion planner augmented reinforcement learning for robot manipulation in obstructed environments,” in Conference on Robot Learning . PMLR, 2021, pp. 589–603
2021
Earlier work this paper cites.
P. Dhariwal and A. Nichol, “Diffusion models beat gans on image synthesis,” Advances in neural information processing systems , vol. 34, pp. 8780–8794, 2021
2021
Earlier work this paper cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. , “Learning transferable visual models from natural language supervision,” in International conference on machine learning . PMLR, 2021, pp. 8748–8763
2021
Earlier work this paper cites.
J. Song, C. Meng, and S. Ermon, “Denoising diffusion implicit models,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 . OpenReview.net, 2021. [Online]. Available: https://openreview.net/forum?id=St1giarCHLP
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
M. Shridhar, L. Manuelli, and D. Fox, “Cliport: What and where pathways for robotic manipulation,” in Conference on robot learning . PMLR, 2022, pp. 894–906
2022
Earlier work this paper cites.
2022
Earlier work this paper cites.
R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
Earlier work this paper cites.
2023
Earlier work this paper cites.
H. R. Walke, K. Black, T. Z. Zhao, Q. Vuong, C. Zheng, P. Hansen-Estruch, A. W. He, V. Myers, M. J. Kim, M. Du, et al. , “Bridgedata v2: A dataset for robot learning at scale,” in Conference on Robot Learning . PMLR, 2023, pp. 1723–1736
2023
Earlier work this paper cites.
2023
Earlier work this paper cites.
——, “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.
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
Earlier work this paper cites.
2023
Earlier work this paper cites.
H.-S. Fang, H. Fang, Z. Tang, J. Liu, J. Wang, H. Zhu, and C. Lu, “Rh20t: A robotic dataset for learning diverse skills in one-shot,” in RSS 2023 Workshop on Learning for Task and Motion Planning , 2023
2023
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2023
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2023
Cited alongside, same era.
2023
Cited alongside, same era.
2024
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2024
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2024
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T. Brooks, B. Peebles, C. Holmes, W. DePue, Y. Guo, L. Jing, D. Schnurr, J. Taylor, T. Luhman, E. Luhman, C. Ng, R. Wang, and A. Ramesh, “Video generation models as world simulators,” 2024. [Online]. Available: https://openai.com/research/video-generation-models-as-world-simulators
2024
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H. Ha, P. Florence, and S. Song, “Scaling up and distilling down: Language-guided robot skill acquisition,” in Conference on Robot Learning . PMLR, 2023, pp. 3766–3777
2023
Cited alongside, same era.
V. Myers, A. W. He, K. Fang, H. R. Walke, P. Hansen-Estruch, C.-A. Cheng, M. Jalobeanu, A. Kolobov, A. Dragan, and S. Levine, “Goal representations for instruction following: A semi-supervised language interface to control,” in Conference on Robot Learning . PMLR, 2023, pp. 3894–3908
2023
Cited alongside, same era.
W. Peebles and S. Xie, “Scalable diffusion models with transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 4195–4205
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
J. Li, D. Li, S. Savarese, and S. Hoi, “Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models,” in International conference on machine learning . PMLR, 2023, pp. 19 730–19 742
2023
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2023
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2023
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Z. Liang, Y. Mu, H. Ma, M. Tomizuka, M. Ding, and P. Luo, “Skilldiffuser: Interpretable hierarchical planning via skill abstractions in diffusion-based task execution,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 16 467–16 476
2024
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2024
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2024
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Y. Ze, G. Zhang, K. Zhang, C. Hu, M. Wang, and H. Xu, “3d diffusion policy: Generalizable visuomotor policy learning via simple 3d representations,” in ICRA 2024 Workshop on 3D Visual Representations for Robot Manipulation , 2024
2024
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2024
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M. Reuss, Ö. E. Yağmurlu, F. Wenzel, and R. Lioutikov, “Multimodal diffusion transformer: Learning versatile behavior from multimodal goals,” in First Workshop on Vision-Language Models for Navigation and Manipulation at ICRA 2024 , 2024
2024
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2024
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2024
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2024
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2025
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2025
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P. Li, H. Wu, Y. Huang, C. Cheang, L. Wang, and T. Kong, “Gr-mg: Leveraging partially-annotated data via multi-modal goal-conditioned policy,” IEEE Robotics and Automation Letters , 2025
2025
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2025
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2025
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L. Chen, S. Bahl, and D. Pathak, “Playfusion: Skill acquisition via diffusion from language-annotated play,” in Conference on Robot Learning . PMLR, 2023, pp. 2012–2029
2029
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