2000 character limit reached
Uncertainty-Aware Deployment of Pre-trained Language-Conditioned Imitation Learning Policies
Published 27 Mar 2024 in cs.RO and cs.LG | (2403.18222v2)
Abstract: Large-scale robotic policies trained on data from diverse tasks and robotic platforms hold great promise for enabling general-purpose robots; however, reliable generalization to new environment conditions remains a major challenge. Toward addressing this challenge, we propose a novel approach for uncertainty-aware deployment of pre-trained language-conditioned imitation learning agents. Specifically, we use temperature scaling to calibrate these models and exploit the calibrated model to make uncertainty-aware decisions by aggregating the local information of candidate actions. We implement our approach in simulation using three such pre-trained models, and showcase its potential to significantly enhance task completion rates. The accompanying code is accessible at the link: https://github.com/BobWu1998/uncertainty_quant_all.git
- S. Yang, O. Nachum, Y. Du, J. Wei, P. Abbeel, and D. Schuurmans, “Foundation models for decision making: Problems, methods, and opportunities,” arXiv preprint arXiv:2303.04129, 2023.
- S. Dasari, F. Ebert, S. Tian, S. Nair, B. Bucher, K. Schmeckpeper, S. Singh, S. Levine, and C. Finn, “Robonet: Large-scale multi-robot learning,” arXiv preprint arXiv:1910.11215, 2019.
- F. Ebert, Y. Yang, K. Schmeckpeper, B. Bucher, G. Georgakis, K. Daniilidis, C. Finn, and S. Levine, “Bridge data: Boosting generalization of robotic skills with cross-domain datasets,” arXiv preprint arXiv:2109.13396, 2021.
- A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, J. Dabis, C. Finn, K. Gopalakrishnan, K. Hausman, A. Herzog, J. Hsu et al., “Rt-1: Robotics transformer for real-world control at scale,” arXiv preprint arXiv:2212.06817, 2022.
- A. Brohan, N. Brown, J. Carbajal, Y. Chebotar, X. Chen, K. Choromanski, T. Ding, D. Driess, A. Dubey, C. Finn et al., “Rt-2: Vision-language-action models transfer web knowledge to robotic control,” arXiv preprint arXiv:2307.15818, 2023.
- M. Shridhar, L. Manuelli, and D. Fox, “Perceiver-actor: A multi-task transformer for robotic manipulation,” in Conference on Robot Learning. PMLR, 2023, pp. 785–799.
- A. Goyal, J. Xu, Y. Guo, V. Blukis, Y.-W. Chao, and D. Fox, “Rvt: Robotic view transformer for 3d object manipulation,” arXiv preprint arXiv:2306.14896, 2023.
- 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.
- Y. Ding, C. Florensa, P. Abbeel, and M. Phielipp, “Goal-conditioned imitation learning,” Advances in neural information processing systems, vol. 32, 2019.
- S. Stepputtis, J. Campbell, M. Phielipp, S. Lee, C. Baral, and H. Ben Amor, “Language-conditioned imitation learning for robot manipulation tasks,” Advances in Neural Information Processing Systems, vol. 33, pp. 13 139–13 150, 2020.
- C. Lynch and P. Sermanet, “Language conditioned imitation learning over unstructured data,” arXiv preprint arXiv:2005.07648, 2020.
- S. Reed, K. Zolna, E. Parisotto, S. G. Colmenarejo, A. Novikov, G. Barth-Maron, M. Gimenez, Y. Sulsky, J. Kay, J. T. Springenberg et al., “A generalist agent,” arXiv preprint arXiv:2205.06175, 2022.
- D. Driess, F. Xia, M. S. Sajjadi, C. Lynch, A. Chowdhery, B. Ichter, A. Wahid, J. Tompson, Q. Vuong, T. Yu et al., “Palm-e: An embodied multimodal language model,” arXiv preprint arXiv:2303.03378, 2023.
- Y. Jiang, A. Gupta, Z. Zhang, G. Wang, Y. Dou, Y. Chen, L. Fei-Fei, A. Anandkumar, Y. Zhu, and L. Fan, “Vima: General robot manipulation with multimodal prompts,” arXiv preprint arXiv:2210.03094, 2022.
- D. Shah, A. Sridhar, N. Dashora, K. Stachowicz, K. Black, N. Hirose, and S. Levine, “Vint: A foundation model for visual navigation,” arXiv preprint arXiv:2306.14846, 2023.
- L. Lindemann, M. Cleaveland, G. Shim, and G. J. Pappas, “Safe planning in dynamic environments using conformal prediction,” IEEE Robotics and Automation Letters, 2023.
- A. Dixit, L. Lindemann, S. X. Wei, M. Cleaveland, G. J. Pappas, and J. W. Burdick, “Adaptive conformal prediction for motion planning among dynamic agents,” in Learning for Dynamics and Control Conference. PMLR, 2023, pp. 300–314.
- J. Lekeufack, A. A. Angelopoulos, A. Bajcsy, M. I. Jordan, and J. Malik, “Conformal decision theory: Safe autonomous decisions from imperfect predictions,” arXiv preprint arXiv:2310.05921, 2023.
- C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in International conference on machine learning. PMLR, 2017, pp. 1321–1330.
- G. Shafer and V. Vovk, “A tutorial on conformal prediction.” Journal of Machine Learning Research, vol. 9, no. 3, 2008.
- A. Z. Ren, A. Dixit, A. Bodrova, S. Singh, S. Tu, N. Brown, P. Xu, L. Takayama, F. Xia, J. Varley et al., “Robots that ask for help: Uncertainty alignment for large language model planners,” arXiv preprint arXiv:2307.01928, 2023.
- R. Firoozi, J. Tucker, S. Tian, A. Majumdar, J. Sun, W. Liu, Y. Zhu, S. Song, A. Kapoor, K. Hausman et al., “Foundation models in robotics: Applications, challenges, and the future,” arXiv preprint arXiv:2312.07843, 2023.
- S. James, Z. Ma, D. R. Arrojo, and A. J. Davison, “Rlbench: The robot learning benchmark & learning environment,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 3019–3026, 2020.
- A. Zeng, P. Florence, J. Tompson, S. Welker, J. Chien, M. Attarian, T. Armstrong, I. Krasin, D. Duong, V. Sindhwani et al., “Transporter networks: Rearranging the visual world for robotic manipulation,” in Conference on Robot Learning. PMLR, 2021, pp. 726–747.
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.