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Deep RL for Autonomous Robots: Limitations and Safety Challenges
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems.
Linköping University, Department of Computer and Information Science, Artificial Intelligence and Integrated Computer Systems. Linköping University, Faculty of Science & Engineering.
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

With the rise of deep reinforcement learning, there has also been a string of successes on continuous control problems using physics simulators. This has lead to some optimism regarding use in autonomous robots and vehicles. However, to successful apply such techniques to the real world requires a firm grasp of their limitations. As recent work has raised questions of how diverse these simulation benchmarks really are, we here instead analyze a popular deep RL approach on toy examples from robot obstacle avoidance. We find that these converge very slowly, if at all, to safe policies. We identify convergence issues on stochastic environments and local minima as problems that warrant more attention for safety-critical control applications.

Place, publisher, year, edition, pages
2019. p. 489-495
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-164581OAI: oai:DiVA.org:liu-164581DiVA, id: diva2:1417203
Conference
European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile CommunicationsAvailable from: 2020-03-26 Created: 2020-03-26 Last updated: 2020-04-06
In thesis
1. Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
Open this publication in new window or tab >>Learning to Make Safe Real-Time Decisions Under Uncertainty for Autonomous Robots
2020 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Robots are increasingly expected to go beyond controlled environments in laboratories and factories, to act autonomously in real-world workplaces and public spaces. Autonomous robots navigating the real world have to contend with a great deal of uncertainty, which poses additional challenges. Uncertainty in the real world accrues from several sources. Some of it may originate from imperfect internal models of reality. Other uncertainty is inherent, a direct side effect of partial observability induced by sensor limitations and occlusions. Regardless of the source, the resulting decision problem is unfortunately computationally intractable under uncertainty. This poses a great challenge as the real world is also dynamic. It  will not pause while the robot computes a solution. Autonomous robots navigating among people, for example in traffic, need to be able to make split-second decisions. Uncertainty is therefore often neglected in practice, with potentially catastrophic consequences when something unexpected happens. The aim of this thesis is to leverage recent advances in machine learning to compute safe real-time approximations to decision-making under uncertainty for real-world robots. We explore a range of methods, from probabilistic to deep learning, as well as different combinations with optimization-based methods from robotics, planning and control. Driven by applications in robot navigation, and grounded in experiments with real autonomous quadcopters, we address several parts of this problem. From reducing uncertainty by learning better models, to directly approximating the decision problem itself, all the while attempting to satisfy both the safety and real-time requirements of real-world autonomy.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2020. p. 55
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2051
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:liu:diva-163419 (URN)10.3384/diss.diva-163419 (DOI)9789179298890 (ISBN)
Public defence
2020-04-29, Ada Lovelace, hus B, Linköpings Universitet, Campus Valla, Linköping, 13:15 (English)
Opponent
Supervisors
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)Swedish Foundation for Strategic Research ELLIIT - The Linköping‐Lund Initiative on IT and Mobile Communications
Available from: 2020-04-06 Created: 2020-03-26 Last updated: 2020-04-06Bibliographically approved

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CiteExportLink to record
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Citation style
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