Sparsity-Inducing Optimal Control via Differential Dynamic Programming

Traiko Dinev, Wolfgang Merkt, Vladimir Ivan, Ioannis Havoutis, Sethu Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Optimal control is a popular approach to synthesize highly dynamic motion. Commonly, L2 regularization is used on the control inputs in order to minimize energy used and to ensure smoothness of the control inputs. However, for some systems, such as satellites, the control needs to be applied in sparse bursts due to how the propulsion system operates. In this paper, we study approaches to induce sparsity in optimal control solutions—namely via smooth L1 and Huber regularization penalties. We apply these loss terms to state-of-the-art Differential Dynamic Programming (DDP)-based solvers to create a family of sparsity-inducing optimal control methods. We analyze and compare the effect of the different losses on inducing sparsity, their numerical conditioning, their impact on convergence, and discuss hyperparameter settings. We demonstrate our method in simulation and hardware experiments on canonical dynamics systems, control of satellites, and the NASA Valkyrie humanoid robot. We provide an implementation of our method and all examples for reproducibility on GitHub.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
Publication statusAccepted/In press - 28 Feb 2021
Event2021 IEEE International Conference on Robotics and Automation - Xi'an, China
Duration: 30 May 20215 Jun 2021
http://www.icra2021.org/

Conference

Conference2021 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2021
CountryChina
CityXi'an
Period30/05/215/06/21
Internet address

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