The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Control of a Quadrotor With Reinforcement Learning
Open accessAbstract
In this letter, we present a method to control a quadrotor with a neural network trained using reinforcement learning techniques. With reinforcement learning, a common network can be trained to directly map state to actuator command making any predefined control structure obsolete for training. Moreover, we present a new learning algorithm that differs from the existing ones in certain aspects. Our algorithm is conservative but stable for complicated tasks. We found that it is more applicable to controlling a quadrotor than existing algorithms. We demonstrate the performance of the trained policy both in simulation and with a real quadrotor. Experiments show that our policy network can react to step response relatively accurately. With the same policy, we also demonstrate that we can stabilize the quadrotor in the air even under very harsh initialization (manually throwing it upside-down in the air with an initial velocity of 5 m/s). Computation time of evaluating the policy is only 7 μs per time step, which is two orders of magnitude less than common trajectory optimization algorithms with an approximated model.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Human-level control through deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., et al. (2015) · Nature · doi:10.1038/nature14236 · Cited by 30,357
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., & Wierstra, D. (2015) · arXiv · doi:10.48550/arxiv.1509.02971 · Cited by 10,782
Robust estimation of a location parameter
Huber, P. J. (1964) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177703732 · Cited by 7,228
Natural Gradient Works Efficiently in Learning
Amari, S.-I. (1998) · Neural Computation · doi:10.1162/089976698300017746 · Cited by 2,681
Benchmarking Deep Reinforcement Learning for Continuous Control
Duan, Y., Chen, X., Houthooft, R., Schulman, J., & Abbeel, P. (2016) · arXiv · doi:10.48550/arxiv.1604.06778 · Cited by 1,114
A robust and modular multi-sensor fusion approach applied to MAV navigation
Lynen, S., Achtelik, M. W., Weiss, S., Chli, M., & Siegwart, R. (2013) · 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2013.6696917 · Cited by 572
Self-citationLearning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Zhang, T., Kahn, G., Levine, S., & Abbeel, P. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487175 · Cited by 452 · cited 1×
“Guided policy search with a MPC controller [5] is demonstrated in simulation.”
MethodOnboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments
Achtelik, M., Achtelik, M., Weiss, S., & Siegwart, R. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980343 · Cited by 295
Self-citation
Cited by
Learning agile and dynamic motor skills for legged robots
Hwangbo, J., Lee, J., Dosovitskiy, A., Bellicoso, D., Tsounis, V., Koltun, V., & Hutter, M. (2019) · Science Robotics · doi:10.1126/scirobotics.aau5872 · Cited by 1,482
Self-citationReinforcement Learning for UAV Attitude Control
Koch, W., Mancuso, R., West, R., & Bestavros, A. (2019) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3301273 · Cited by 499
The current state and future outlook of rescue robotics
Delmerico, J., Mintchev, S., Giusti, A., Gromov, B., Melo, K., Horvat, T., Cadena, C., Hutter, M., Ijspeert, A., Floreano, D., et al. (2019) · Journal of Field Robotics · doi:10.1002/rob.21887 · Cited by 371
Self-citationweedNet: Dense Semantic Weed Classification Using Multispectral Images and MAV for Smart Farming
Sa, I., Chen, Z., Popovic, M., Khanna, R., Liebisch, F., Nieto, J., & Siegwart, R. (2018) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2774979 · Cited by 369
Self-citationDynamic and Multi-faceted Spatio-temporal Deep Learning for Traffic Speed Forecasting
Han, L., Du, B., Sun, L., Fu, Y., Lv, Y., & Xiong, H. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467275 · Cited by 281
Metadata
- Authors
- Hwangbo, J., Sa, I., Siegwart, R., & Hutter, M.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2017-10
- Volume
- 2
- Issue
- 4
- Pages
- 2096-2103
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2733312032
- Cited by
- 543
- References
- 13
- FWCI
- 20.83
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Control of a Quadrotor With Reinforcement Learning. https://app.cochat.ai/papers/10.1109/lra.2017.2720851
- The paper
- Hwangbo, J., Sa, I., Siegwart, R., & Hutter, M. (2017). Control of a Quadrotor With Reinforcement Learning. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2017.2720851
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
