The DOI matches its Crossref record; 43 of the 45 references it cites were found in a registry or an index.
Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search
Open accessAbstract
Model predictive control (MPC) is an effective method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters. However, application of MPC can be computationally demanding, and typically requires estimating the state of the system, which can be challenging in complex, unstructured environments. Reinforcement learning can in principle forego the need for explicit state estimation and acquire a policy that directly maps sensor readings to actions, but is difficult to apply to unstable systems that are liable to fail catastrophically during training before an effective policy has been found. We propose to combine MPC with reinforcement learning in the framework of guided policy search, where MPC is used to generate data at training time, under full state observations provided by an instrumented training environment. This data is used to train a deep neural network policy, which is allowed to access only the raw observations from the vehicle's onboard sensors. After training, the neural network policy can successfully control the robot without knowledge of the full state, and at a fraction of the computational cost of MPC. We evaluate our method by learning obstacle avoidance policies for a simulated quadrotor, using simulated onboard sensors and no explicit state estimation at test time.
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In the citing paper
“The frequent sensing and re-planning allow the robot to react dynamically to changes in the environment, while still making more intelligent choices than a purely reactive system (Erez et al., 2011; Lenz et al., 2015; Zhang et al., 2016; Liu et al., 2017; Williams et al., 2017; Gillespie et al., 2018; Amos et al., 2019; Kabzan et al., 2019).”
From Lessons for Robotics From the Control Architecture of the Octopus (2022) · cites this paper as Zhang et al., 2016
In the citing paper
“AField-Programmable Gate Array (FPGA) is another viable solution, showing great improvements in both performance and power consumption in deep learning applications.”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [170]
In the citing paper
“Currently, the largest portion of papers in deep learning for selfdriving cars are focused mainly on perception and End2End learning [81, 124].”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [124]
In the citing paper
“Finally, realtime requirements have to be fulfilled for processing the large amounts of data gathered from the car's sensors suite, as well as for updating the parameters of deep learning systems over high-speed communication lines [170].”
From A survey of deep learning techniques for autonomous driving (2020) · cites this paper as [170]
Citations
Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 2×
“We used default values for all learning parameters as presented in [14].”
Method(2010) · International Conference on Machine Learning · Cited by 13,995
Playing Atari with Deep Reinforcement Learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013) · arXiv · doi:10.48550/arxiv.1312.5602 · Cited by 9,471 · cited 1×
“More recently, deep neural network policies have been used for tasks ranging from robotic control [22], [23] to video game playing [27].”
MethodMuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., & Tassa, Y. (2012) · 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2012.6386109 · Cited by 4,767 · cited 2×
“However, our derivation largely follows prior work [41].”
MethodResultReinforcement learning in robotics: A survey
Kober, J., Bagnell, J. A., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913495721 · Cited by 3,160 · cited 1×
“An overview of recent reinforcement learning methods in robotics can be found in a recent survey paper [15].”
BackgroundEnd-to-End Training of Deep Visuomotor PoliciesPreprint
Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1504.00702 · Cited by 2,641 · cited 10×
“In this paper, we use the BADMM version, which specifies the following objective for trajectory optimization: min pi(τ) T∑ t=1 Epi(xt,ut) [ `(xt,ut)− uTt λiµt + νitDKL ( pi(ut|xt)‖πθ(ut|xt) )] (1) where λµt is a Lagrange multiplier on the mean action, and the third term is a KL-divergence penalty.”
BackgroundMethodInfluentialSelf-citationA Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning
Ross, S., Gordon, G. J., & Bagnell, J. A. (2018) · Carnegie Mellon University · doi:10.1184/r1/6550949 · Cited by 1,941
Neural networks for control systems—A survey
Hunt, K. J., Sbarbaro, D. G., Żbikowski, R., & Gawthrop, P. J. (1992) · Automatica · doi:10.1016/0005-1098(92)90053-i · Cited by 1,874 · cited 1×
“neural networks have been used for control for decades [12], [33], limitations on computation and algorithms have made large neural network policies very difficult to learn.”
MethodRobust model predictive control of constrained linear systems with bounded disturbances
Mayne, D. Q., Seron, M. M., & Raković, S. V. (2004) · Automatica · doi:10.1016/j.automatica.2004.08.019 · Cited by 1,680 · cited 1×
“Model predictive control (MPC) is an effective and reliable method for controlling robotic systems, particularly autonomous aerial vehicles such as quadcopters, because of its robustness to moderate model errors [26], ability to use high-level objectives [41], and relative simplicity.”
MethodALVINN, an autonomous land vehicle in a neural network
Pomerleau, D. (2018) · doi:10.1184/r1/6603146 · Cited by 1,369
Flagged first, then by citations
2 of the 45 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
Sze, V., Chen, Y.-H., Yang, T.-J., & Emer, J. S. (2017) · Proceedings of the IEEE · doi:10.1109/jproc.2017.2761740 · Cited by 4,215
“• Robotics DNNs have been successful in the domain of robotic tasks such as grasping with a robotic arm [32], motion planning for ground robots [33], visual navigation [4, 34], control to stabilize a quadcopter [35] and driving strategies for autonomous vehicles [36].”
A survey of deep learning techniques for autonomous driving
Grigorescu, S., Trasnea, B., Cocias, T., & Macesanu, G. (2020) · Journal of Field Robotics · doi:10.1002/rob.21918 · Cited by 1,809
“rently, the largest portion of papers in deep learning for selfdriving cars are focused mainly on perception and End2End learning [81, 124].”
BackgroundInfluentialData-Driven Science and Engineering
Brunton, S. L., & Kutz, J. N. (2019) · Cambridge University Press eBooks · doi:10.1017/9781108380690 · Cited by 1,269
Socially aware motion planning with deep reinforcement learning
Chen, Y. F., Everett, M., Liu, M., & How, J. P. (2017) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8202312 · Cited by 743
“While several recent works have applied deep RL to motion planning [22], [23], they are mainly focused on single agent navigation in unknown static environments, and with an emphasis on computing control inputs directly from raw”
BackgroundDecentralized non-communicating multiagent collision avoidance with deep reinforcement learning
Chen, Y. F., Liu, M., Everett, M., & How, J. P. (2017) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2017.7989037 · Cited by 689
“While applications of RL are typically limited to discrete, low-dimensional domains, recent advances in Deep RL [20]– [23] have demonstrated human-level performance in complex, high-dimensional spaces.”
Background
Metadata
- Authors
- Zhang, T., Kahn, G., Levine, S., & Abbeel, P.
- Affiliations
- University of California, Berkeley (US)
- Venue
- 2016 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2016-05
- Pages
- 528-535
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W1923344279
- Cited by
- 452
- References
- 45
- Citations by year
- 2026: 10 · 2025: 18 · 2024: 37 · 2023: 33 · 2022: 54 · 2021: 53 · 2020: 65 · 2019: 93 · 2018: 42 · 2017: 39 · 2016: 8
- FWCI
- 32.08
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Proceedings - IEEE International Conference on Robotics and Automation/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Defense Advanced Research Projects Agency (N66001)
- Army Research Office
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Engineering › Control and Systems Engineering › Adaptive Dynamic Programming Control
- Physical Sciences › Engineering › Control and Systems Engineering › Advanced Control Systems Optimization
- Keywords
- model predictive control, deep neural network policies, autonomous aerial vehicles, quadrotor control, obstacle avoidance, reinforcement learning, state estimation
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