The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
BADGR: An Autonomous Self-Supervised Learning-Based Navigation System
Open accessAbstract
Mobile robot navigation is typically regarded as a geometric problem, in which the robot's objective is to perceive the geometry of the environment in order to plan collision-free paths towards a desired goal. However, a purely geometric view of the world can be insufficient for many navigation problems. For example, a robot navigating based on geometry may avoid a field of tall grass because it believes it is untraversable, and will therefore fail to reach its desired goal. In this work, we investigate how to move beyond these purely geometric-based approaches using a method that learns about physical navigational affordances from experience. Our reinforcement learning approach, which we call BADGR, is an end-to-end learning-based mobile robot navigation system that can be trained with autonomously-labeled off-policy data gathered in real-world environments, without any simulation or human supervision. BADGR can navigate in real-world urban and off-road environments with geometrically distracting obstacles. It can also incorporate terrain preferences, generalize to novel environments, and continue to improve autonomously by gathering more data. Videos, code, and other supplemental material are available on our website https://sites.google.com/view/badgr.
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Cited by
Deep Reinforcement Learning: A Survey
Wang, X., Wang, S., Liang, X., Zhao, D., Huang, J., Xu, X., Dai, B., & Miao, Q. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3207346 · Cited by 972
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Self-citationWILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
“Robot learning has emerged as a strong paradigm for automatically acquiring complex and skilled behaviors such as locomotion (Yang et al., 2019; Peng et al., 2020), navigation (Mirowski et al., 2017; Kahn et al., 2020), and manipulation (Gu et al., 2017; et al, 2019).”
Self-citationMorales, E. F., Murrieta-Cid, R., Becerra, I., & Esquivel-Basaldua, M. A. (2021) · Intelligent Service Robotics · doi:10.1007/s11370-021-00398-z · Cited by 101
Ving: Learning open-world navigation with visual goals
Shah, D., Eysenbach, B., Kahn, G., Rhinehart, N., & Levine, S. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561936 · Cited by 81
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Metadata
- Authors
- Kahn, G., Abbeel, P., & Levine, S.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2021-04
- Volume
- 6
- Issue
- 2
- Pages
- 1312-1319
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W3006388420
- Cited by
- 284
- References
- 39
- FWCI
- 17.73
- Type
- journal article
- Language
- en
- Open access
- green
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