Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
119 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Domain Adaptation of Visual Policies with a Single Demonstration (2407.16820v1)

Published 23 Jul 2024 in cs.RO

Abstract: Deploying machine learning algorithms for robot tasks in real-world applications presents a core challenge: overcoming the domain gap between the training and the deployment environment. This is particularly difficult for visuomotor policies that utilize high-dimensional images as input, particularly when those images are generated via simulation. A common method to tackle this issue is through domain randomization, which aims to broaden the span of the training distribution to cover the test-time distribution. However, this approach is only effective when the domain randomization encompasses the actual shifts in the test-time distribution. We take a different approach, where we make use of a single demonstration (a prompt) to learn policy that adapts to the testing target environment. Our proposed framework, PromptAdapt, leverages the Transformer architecture's capacity to model sequential data to learn demonstration-conditioned visual policies, allowing for in-context adaptation to a target domain that is distinct from training. Our experiments in both simulation and real-world settings show that PromptAdapt is a strong domain-adapting policy that outperforms baseline methods by a large margin under a range of domain shifts, including variations in lighting, color, texture, and camera pose. Videos and more information can be viewed at project webpage: https://sites.google.com/view/promptadapt.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (54)
  1. D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke et al., “Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation,” arXiv preprint arXiv:1806.10293, 2018.
  2. J. Kulhánek, E. Derner, T. De Bruin, and R. Babuška, “Vision-based navigation using deep reinforcement learning,” in 2019 european conference on mobile robots (ECMR).   IEEE, 2019, pp. 1–8.
  3. N. Hansen, H. Su, and X. Wang, “Stabilizing deep q-learning with convnets and vision transformers under data augmentation,” Advances in Neural Information Processing Systems, vol. 34, pp. 3680–3693, 2021.
  4. E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ international conference on intelligent robots and systems.   IEEE, 2012, pp. 5026–5033.
  5. V. Makoviychuk, L. Wawrzyniak, Y. Guo, M. Lu, K. Storey, M. Macklin, D. Hoeller, N. Rudin, A. Allshire, A. Handa, and G. State, “Isaac gym: High performance gpu-based physics simulation for robot learning,” 2021.
  6. W. Zhao, J. P. Queralta, and T. Westerlund, “Sim-to-real transfer in deep reinforcement learning for robotics: a survey,” in 2020 IEEE symposium series on computational intelligence (SSCI).   IEEE, 2020, pp. 737–744.
  7. K. Cobbe, C. Hesse, J. Hilton, and J. Schulman, “Leveraging procedural generation to benchmark reinforcement learning,” in International conference on machine learning.   PMLR, 2020, pp. 2048–2056.
  8. Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The journal of machine learning research, vol. 17, no. 1, pp. 2096–2030, 2016.
  9. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7167–7176.
  10. J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS).   IEEE, 2017, pp. 23–30.
  11. X. Chen, J. Hu, C. Jin, L. Li, and L. Wang, “Understanding domain randomization for sim-to-real transfer,” arXiv preprint arXiv:2110.03239, 2021.
  12. L. Fan, G. Wang, D.-A. Huang, Z. Yu, L. Fei-Fei, Y. Zhu, and A. Anandkumar, “Secant: Self-expert cloning for zero-shot generalization of visual policies,” arXiv preprint arXiv:2106.09678, 2021.
  13. S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in Proceedings of the fourteenth international conference on artificial intelligence and statistics.   JMLR Workshop and Conference Proceedings, 2011, pp. 627–635.
  14. J. Ho and S. Ermon, “Generative adversarial imitation learning,” Advances in neural information processing systems, vol. 29, 2016.
  15. A. A. Rusu, S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell, “Policy distillation,” arXiv preprint arXiv:1511.06295, 2015.
  16. E. Parisotto, J. L. Ba, and R. Salakhutdinov, “Actor-mimic: Deep multitask and transfer reinforcement learning,” arXiv preprint arXiv:1511.06342, 2015.
  17. T. Chen, J. Xu, and P. Agrawal, “A system for general in-hand object re-orientation,” in Conference on Robot Learning.   PMLR, 2022, pp. 297–307.
  18. H. Lai, W. Zhang, X. He, C. Yu, Z. Tian, Y. Yu, and J. Wang, “Sim-to-real transfer for quadrupedal locomotion via terrain transformer,” arXiv preprint arXiv:2212.07740, 2022.
  19. S. Dasari and A. Gupta, “Transformers for one-shot visual imitation,” in Conference on Robot Learning.   PMLR, 2021, pp. 2071–2084.
  20. Z. Mandi, F. Liu, K. Lee, and P. Abbeel, “Towards more generalizable one-shot visual imitation learning,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 2434–2444.
  21. M. Xu, Y. Shen, S. Zhang, Y. Lu, D. Zhao, J. Tenenbaum, and C. Gan, “Prompting decision transformer for few-shot policy generalization,” in International Conference on Machine Learning.   PMLR, 2022, pp. 24 631–24 645.
  22. C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel, “Deep spatial autoencoders for visuomotor learning,” in 2016 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2016, pp. 512–519.
  23. X. Lin, H. Baweja, G. Kantor, and D. Held, “Adaptive auxiliary task weighting for reinforcement learning,” Advances in neural information processing systems, vol. 32, 2019.
  24. C. Lyle, M. Rowland, G. Ostrovski, and W. Dabney, “On the effect of auxiliary tasks on representation dynamics,” in International Conference on Artificial Intelligence and Statistics.   PMLR, 2021, pp. 1–9.
  25. T. He, Y. Zhang, K. Ren, M. Liu, C. Wang, W. Zhang, Y. Yang, and D. Li, “Reinforcement learning with automated auxiliary loss search,” arXiv preprint arXiv:2210.06041, 2022.
  26. I. Kostrikov, D. Yarats, and R. Fergus, “Image augmentation is all you need: Regularizing deep reinforcement learning from pixels,” arXiv preprint arXiv:2004.13649, 2020.
  27. N. Hansen and X. Wang, “Generalization in reinforcement learning by soft data augmentation,” in 2021 IEEE International Conference on Robotics and Automation (ICRA).   IEEE, 2021, pp. 13 611–13 617.
  28. G. Ma, Z. Wang, Z. Yuan, X. Wang, B. Yuan, and D. Tao, “A comprehensive survey of data augmentation in visual reinforcement learning,” arXiv preprint arXiv:2210.04561, 2022.
  29. D. Bertoin, A. Zouitine, M. Zouitine, and E. Rachelson, “Look where you look! saliency-guided q-networks for visual rl tasks,” in Thirty-sixth Conference on Neural Information Processing Systems (NeurIPS 2022), 2022.
  30. J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning quadrupedal locomotion over challenging terrain,” Science robotics, vol. 5, no. 47, p. eabc5986, 2020.
  31. T. Miki, J. Lee, J. Hwangbo, L. Wellhausen, V. Koltun, and M. Hutter, “Learning robust perceptive locomotion for quadrupedal robots in the wild,” Science Robotics, vol. 7, no. 62, p. eabk2822, 2022.
  32. V. Lim, H. Huang, L. Y. Chen, J. Wang, J. Ichnowski, D. Seita, M. Laskey, and K. Goldberg, “Planar robot casting with real2sim2real self-supervised learning,” arXiv preprint arXiv:2111.04814, 2021.
  33. ——, “Real2sim2real: Self-supervised learning of physical single-step dynamic actions for planar robot casting,” in 2022 International Conference on Robotics and Automation (ICRA).   IEEE, 2022, pp. 8282–8289.
  34. W. Yu, J. Tan, C. K. Liu, and G. Turk, “Preparing for the unknown: Learning a universal policy with online system identification,” arXiv preprint arXiv:1702.02453, 2017.
  35. A. Kumar, Z. Fu, D. Pathak, and J. Malik, “Rma: Rapid motor adaptation for legged robots,” arXiv preprint arXiv:2107.04034, 2021.
  36. N. Hansen, R. Jangir, Y. Sun, G. Alenyà, P. Abbeel, A. A. Efros, L. Pinto, and X. Wang, “Self-supervised policy adaptation during deployment,” arXiv preprint arXiv:2007.04309, 2020.
  37. K. Rao, C. Harris, A. Irpan, S. Levine, J. Ibarz, and M. Khansari, “Rl-cyclegan: Reinforcement learning aware simulation-to-real,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020, pp. 11 157–11 166.
  38. D. Ho, K. Rao, Z. Xu, E. Jang, M. Khansari, and Y. Bai, “Retinagan: An object-aware approach to sim-to-real transfer,” 2020. [Online]. Available: https://arxiv.org/abs/2011.03148
  39. T. Yoneda, G. Yang, M. R. Walter, and B. Stadie, “Invariance through inference,” arXiv preprint arXiv:2112.08526, 2021.
  40. K. Rakelly, A. Zhou, C. Finn, S. Levine, and D. Quillen, “Efficient off-policy meta-reinforcement learning via probabilistic context variables,” in International conference on machine learning.   PMLR, 2019, pp. 5331–5340.
  41. T. Hospedales, A. Antoniou, P. Micaelli, and A. Storkey, “Meta-learning in neural networks: A survey,” IEEE transactions on pattern analysis and machine intelligence, vol. 44, no. 9, pp. 5149–5169, 2021.
  42. Y. Wang, Q. Yao, J. T. Kwok, and L. M. Ni, “Generalizing from a few examples: A survey on few-shot learning,” ACM computing surveys (csur), vol. 53, no. 3, pp. 1–34, 2020.
  43. C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in International conference on machine learning.   PMLR, 2017, pp. 1126–1135.
  44. Y. Duan, M. Andrychowicz, B. Stadie, O. Jonathan Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba, “One-shot imitation learning,” Advances in neural information processing systems, vol. 30, 2017.
  45. L. Kirsch, S. van Steenkiste, and J. Schmidhuber, “Improving generalization in meta reinforcement learning using learned objectives,” arXiv preprint arXiv:1910.04098, 2019.
  46. X. Song, Y. Yang, K. Choromanski, K. Caluwaerts, W. Gao, C. Finn, and J. Tan, “Rapidly adaptable legged robots via evolutionary meta-learning,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).   IEEE, 2020, pp. 3769–3776.
  47. A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems, vol. 30, 2017.
  48. T. Lin, Y. Wang, X. Liu, and X. Qiu, “A survey of transformers,” AI Open, 2022.
  49. P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” arXiv preprint arXiv:2107.13586, 2021.
  50. J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou, “Chain of thought prompting elicits reasoning in large language models,” arXiv preprint arXiv:2201.11903, 2022.
  51. L. Chen, K. Lu, A. Rajeswaran, K. Lee, A. Grover, M. Laskin, P. Abbeel, A. Srinivas, and I. Mordatch, “Decision transformer: Reinforcement learning via sequence modeling,” Advances in neural information processing systems, vol. 34, pp. 15 084–15 097, 2021.
  52. T. Haarnoja, A. Zhou, K. Hartikainen, G. Tucker, S. Ha, J. Tan, V. Kumar, H. Zhu, A. Gupta, P. Abbeel et al., “Soft actor-critic algorithms and applications,” arXiv preprint arXiv:1812.05905, 2018.
  53. A. Stone, O. Ramirez, K. Konolige, and R. Jonschkowski, “The distracting control suite–a challenging benchmark for reinforcement learning from pixels,” arXiv preprint arXiv:2101.02722, 2021.
  54. Y. Tassa, Y. Doron, A. Muldal, T. Erez, Y. Li, D. de Las Casas, D. Budden, A. Abdolmaleki, J. Merel, A. Lefrancq, T. Lillicrap, and M. Riedmiller, “Deepmind control suite,” 2018.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (2)
  1. Weiyao Wang (27 papers)
  2. Gregory D. Hager (79 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now