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Optimal Control and Inverse Optimal Control by Distribution Matching

Arenz, Oleg ; Abdulsamad, Hany ; Neumann, Gerhard (2022)
Optimal Control and Inverse Optimal Control by Distribution Matching.
2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Daejeon, Korea (09.-14.10.2016)
doi: 10.26083/tuprints-00022929
Conference or Workshop Item, Secondary publication, Postprint

Abstract

Optimal control is a powerful approach to achieve optimal behavior. However, it typically requires a manual specification of a cost function which often contains several objectives, such as reaching goal positions at different time steps or energy efficiency. Manually trading-off these objectives is often difficult and requires a high engineering effort. In this paper, we present a new approach to specify optimal behavior. We directly specify the desired behavior by a distribution over future states or features of the states. For example, the experimenter could choose to reach certain mean positions with given accuracy/variance at specified time steps. Our approach also unifies optimal control and inverse optimal control in one framework. Given a desired state distribution, we estimate a cost function such that the optimal controller matches the desired distribution. If the desired distribution is estimated from expert demonstrations, our approach performs inverse optimal control. We evaluate our approach on several optimal and inverse optimal control tasks on non-linear systems using incremental linearizations similar to differential dynamic programming approaches.

Item Type: Conference or Workshop Item
Erschienen: 2022
Creators: Arenz, Oleg ; Abdulsamad, Hany ; Neumann, Gerhard
Type of entry: Secondary publication
Title: Optimal Control and Inverse Optimal Control by Distribution Matching
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2016
Publisher: IEEE
Book Title: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Collation: 14 ungezählte Seiten
Event Title: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Event Location: Daejeon, Korea
Event Dates: 09.-14.10.2016
DOI: 10.26083/tuprints-00022929
URL / URN: https://tuprints.ulb.tu-darmstadt.de/22929
Corresponding Links:
Origin: Secondary publication service
Abstract:

Optimal control is a powerful approach to achieve optimal behavior. However, it typically requires a manual specification of a cost function which often contains several objectives, such as reaching goal positions at different time steps or energy efficiency. Manually trading-off these objectives is often difficult and requires a high engineering effort. In this paper, we present a new approach to specify optimal behavior. We directly specify the desired behavior by a distribution over future states or features of the states. For example, the experimenter could choose to reach certain mean positions with given accuracy/variance at specified time steps. Our approach also unifies optimal control and inverse optimal control in one framework. Given a desired state distribution, we estimate a cost function such that the optimal controller matches the desired distribution. If the desired distribution is estimated from expert demonstrations, our approach performs inverse optimal control. We evaluate our approach on several optimal and inverse optimal control tasks on non-linear systems using incremental linearizations similar to differential dynamic programming approaches.

Uncontrolled Keywords: Optimal control, Entropy, Heuristic algorithms, Trajectory, Cost function, Learning (artificial intelligence)
Status: Postprint
URN: urn:nbn:de:tuda-tuprints-229290
Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Intelligent Autonomous Systems
Date Deposited: 25 Nov 2022 12:51
Last Modified: 08 Aug 2023 12:13
PPN: 503350850
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