[14] Ting Chen et al. “A simple framework for contrastive learning of visual representations”. In: Proceedings of the 37th International Conference on Machine Learning. 2020, pp. 1597–1607.
[15] Tianhe Yu et al. "Meta-World: A benchmark and evaluation for multi-task and meta reinforcement learning". In: Proceedings of the 3rd Conference on Robot Learning. 2019, pp. 1094–1100.
[16] Jinwei Xing et al. "Domain adaptation in reinforcement learning via latent unified state representation". In: Proceedings of the 35th AAAI Conference on Artificial Intelligence. 2021, pp. 10452-10459.
[17] Menglin Jia et al. "Visual prompt tuning". In: Proceedings of the 17th European Conference on Computer Vision. 2022, pp. 709-727.
[18] Kaiyang Zhou et al. "Learning to prompt for vision-language models". In: International Journal of Computer Vision (2022).
[19] Bang You et al. "Integrating contrastive learning with dynamic models for reinforcement learning from images". In: Neurocomputing (2022).
[20] Minbeom Kim et al. "Action-driven contrastive representation for reinforcement learning". In: PLOS ONE (2022).
[21] Rishabh Agarwal et al. "Contrastive behavioral similarity embeddings for generalization in reinforcement learning". In: arXiv preprint arXiv:2101.05265 (2021).
[22] Young Jae Lee et al. "STACoRe: Spatio-temporal and action-based contrastive representations for reinforcement learning in Atari". In: Neural Networks (2023).
[23] John Schulman et al. "Proximal policy optimization algorithms". In: arXiv preprint arXiv:1707.06347 (2017).
[24] Stéphane Ross, Geoffrey J. Gordon, and J. Andrew Bagnell. “A reduction of imitation learning and structured prediction to no-regret online learning”. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. 2011, pp. 627–635.
[25] Pengfei Liu et al. "Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing". In: ACM Computing Surveys (2023).
[26] Minh-Thang Luong, Hieu Pham, and Christopher D Manning. "Effective approaches to attention-based neural machine translation". In: arXiv preprint arXiv:1508.04025 (2015).
[27] Ashish Vaswani et al. "Attention is all you need". In: Advances in neural information processing systems (2017).
[28] Denis Yarats, Ilya Kostrikov, and Rob Fergus. "Image Augmentation Is All You Need: Regularizing deep reinforcement learning from pixels". In: Proceedings of the 9th International Conference on Learning Representations. 2021.
[29] Samarth Sinha, Ajay Mandlekar, and Animesh Garg. "S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning in Robotics". In: Proceedings of the 5th Conference on Robotics Learning. 2021, pp. 907-917.
[30] Arjun Majumdar et al. "Where are we in the search for an Artificial Visual Cortex for Embodied Intelligence?" In: arXiv preprint arXiv:2303.18240 (2023).
[31] Suraj Nair et al. “R3m: A universal visual representation for robot manipulation”. In: arXiv preprint arXiv:2203.12601 (2022).
[32] Austin Stone et al. "Open-world object manipulation using pre-trained vision-language models". In: arXiv preprint arXiv:2303.00905 (2023).
[33] Jiange Yang et al. "Pave the Way to Grasp Anything: Transferring Foundation Models for Universal Pick-Place Robots". In: arXiv preprint arXiv:2306.05716 (2023).
[34] Dhruv Shah et al. "Gnm: A general navigation model to drive any robot". In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE. 2023, pp. 7226-7233.
[35] Noriaki Hirose et al. "ExAug: Robot-conditioned navigation policies via geometric experience augmentation". In: Proceedings of the IEEE International Conference on Robotics and Automation. IEEE. 2023, pp. 4077-4084.
[36] Daniel Fried et al. "Speaker-follower models for vision-and-language navigation". In: Proceedings of the 31th Conference on Neural Information Processing Systems. 2018, pp. 3318-3329.
[37] Felix Yu et al. "Take the scenic route: Improving generalization in vision-and-language navigation". In: arXiv preprint arXiv:2003.14269 (2020).