The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Autonomous Helicopter Aerobatics through Apprenticeship Learning
Abstract
Autonomous helicopter flight is widely regarded to be a highly challenging control problem. Despite this fact, human experts can reliably fly helicopters through a wide range of maneuvers, including aerobatic maneuvers at the edge of the helicopter’s capabilities. We present apprenticeship learning algorithms, which leverage expert demonstrations to efficiently learn good controllers for tasks being demonstrated by an expert. These apprenticeship learning algorithms have enabled us to significantly extend the state of the art in autonomous helicopter aerobatics. Our experimental results include the first autonomous execution of a wide range of maneuvers, including but not limited to in-place flips, in-place rolls, loops and hurricanes, and even auto-rotation landings, chaos and tic-tocs, which only exceptional human pilots can perform. Our results also include complete airshows, which require autonomous transitions between many of these maneuvers. Our controllers perform as well as, and often even better than, our expert pilot.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A New Approach to Linear Filtering and Prediction Problems
Kalman, R. E. (1960) · Journal of Basic Engineering · doi:10.1115/1.3662552 · Cited by 31,751
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
Dynamic Programming Algorithm Optimization for Spoken Word Recognition
Sakoe, H., & Chiba, S. (1978) · IEEE Transactions on Acoustics Speech and Signal Processing · doi:10.1109/tassp.1978.1163055 · Cited by 6,727
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
(2000) · doi:10.2460/ajvr.67.2.323 · Cited by 1,621
Cited by
(2015) · Journal of Machine Learning Research · Cited by 1,119
Gibson Env: Real-World Perception for Embodied Agents
Xia, F., Zamir, A. R., He, Z., Sax, A., Malik, J., & Savarese, S. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00945 · Cited by 763
Polynomial Trajectory Planning for Aggressive Quadrotor Flight in Dense Indoor Environments
Richter, C., Bry, A., & Roy, N. (2016) · Springer Tracts in Advanced Robotics · doi:10.1007/978-3-319-28872-7_37 · Cited by 747
CAD2RL: Real Single-Image Flight Without a Single Real Image
Sadeghi, F., & Levine, S. (2017) · Robotics: Science and Systems XIII · doi:10.15607/rss.2017.xiii.034 · Cited by 618
Cheng, R., Orosz, G., Murray, R. M., & Burdick, J. W. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33013387 · Cited by 611
“Previous approaches to safe reinforcement learning include reward-shaping , policy optimization with constraints (Gaskett 2003; Moldovan and Abbeel 2012; Achiam et al. 2017; Wachi et al. 2018), or teacher advice (Abbeel and Ng 2004; Abbeel, Coates, and Ng 2010; Tang et al. 2010).”
Background
Metadata
- Authors
- Abbeel, P., Coates, A., & Ng, A. Y.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2010-06-23
- Volume
- 29
- Issue
- 13
- Pages
- 1608-1639
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2134491302
- Cited by
- 584
- References
- 6
- FWCI
- 26.53
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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