--- _id: '8704' abstract: - lang: eng text: Traditional robotic control suits require profound task-specific knowledge for designing, building and testing control software. The rise of Deep Learning has enabled end-to-end solutions to be learned entirely from data, requiring minimal knowledge about the application area. We design a learning scheme to train end-to-end linear dynamical systems (LDS)s by gradient descent in imitation learning robotic domains. We introduce a new regularization loss component together with a learning algorithm that improves the stability of the learned autonomous system, by forcing the eigenvalues of the internal state updates of an LDS to be negative reals. We evaluate our approach on a series of real-life and simulated robotic experiments, in comparison to linear and nonlinear Recurrent Neural Network (RNN) architectures. Our results show that our stabilizing method significantly improves test performance of LDS, enabling such linear models to match the performance of contemporary nonlinear RNN architectures. A video of the obstacle avoidance performance of our method on a mobile robot, in unseen environments, compared to other methods can be viewed at https://youtu.be/mhEsCoNao5E. acknowledgement: M.L. is supported in parts by the Austrian Science Fund (FWF) under grant Z211-N23 (Wittgenstein Award). R.H., and R.G. are partially supported by the Horizon-2020 ECSELProject grant No. 783163 (iDev40), and the Austrian Research Promotion Agency (FFG), Project No. 860424. R.H. and D.R. is partially supported by the Boeing Company. alternative_title: - ICRA article_processing_charge: No author: - first_name: Mathias full_name: Lechner, Mathias id: 3DC22916-F248-11E8-B48F-1D18A9856A87 last_name: Lechner - first_name: Ramin full_name: Hasani, Ramin last_name: Hasani - first_name: Daniela full_name: Rus, Daniela last_name: Rus - first_name: Radu full_name: Grosu, Radu last_name: Grosu citation: ama: 'Lechner M, Hasani R, Rus D, Grosu R. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In: Proceedings - IEEE International Conference on Robotics and Automation. IEEE; 2020:5446-5452. doi:10.1109/ICRA40945.2020.9196608' apa: 'Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2020). Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. In Proceedings - IEEE International Conference on Robotics and Automation (pp. 5446–5452). Paris, France: IEEE. https://doi.org/10.1109/ICRA40945.2020.9196608' chicago: Lechner, Mathias, Ramin Hasani, Daniela Rus, and Radu Grosu. “Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot Learning Scheme.” In Proceedings - IEEE International Conference on Robotics and Automation, 5446–52. IEEE, 2020. https://doi.org/10.1109/ICRA40945.2020.9196608. ieee: M. Lechner, R. Hasani, D. Rus, and R. Grosu, “Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme,” in Proceedings - IEEE International Conference on Robotics and Automation, Paris, France, 2020, pp. 5446–5452. ista: 'Lechner M, Hasani R, Rus D, Grosu R. 2020. Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme. Proceedings - IEEE International Conference on Robotics and Automation. ICRA: International Conference on Robotics and Automation, ICRA, , 5446–5452.' mla: Lechner, Mathias, et al. “Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-End Robot Learning Scheme.” Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–52, doi:10.1109/ICRA40945.2020.9196608. short: M. Lechner, R. Hasani, D. Rus, R. Grosu, in:, Proceedings - IEEE International Conference on Robotics and Automation, IEEE, 2020, pp. 5446–5452. conference: end_date: 2020-08-31 location: Paris, France name: 'ICRA: International Conference on Robotics and Automation' start_date: 2020-05-31 date_created: 2020-10-25T23:01:19Z date_published: 2020-05-01T00:00:00Z date_updated: 2023-08-22T10:40:15Z day: '01' ddc: - '000' department: - _id: ToHe doi: 10.1109/ICRA40945.2020.9196608 external_id: isi: - '000712319503110' file: - access_level: open_access checksum: fccf7b986ac78046918a298cc6849a50 content_type: application/pdf creator: dernst date_created: 2020-11-06T10:58:49Z date_updated: 2020-11-06T10:58:49Z file_id: '8733' file_name: 2020_ICRA_Lechner.pdf file_size: 1070010 relation: main_file success: 1 file_date_updated: 2020-11-06T10:58:49Z has_accepted_license: '1' isi: 1 language: - iso: eng month: '05' oa: 1 oa_version: Submitted Version page: 5446-5452 project: - _id: 25F42A32-B435-11E9-9278-68D0E5697425 call_identifier: FWF grant_number: Z211 name: The Wittgenstein Prize publication: Proceedings - IEEE International Conference on Robotics and Automation publication_identifier: isbn: - '9781728173955' issn: - '10504729' publication_status: published publisher: IEEE quality_controlled: '1' scopus_import: '1' status: public title: Gershgorin loss stabilizes the recurrent neural network compartment of an end-to-end robot learning scheme type: conference user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8 year: '2020' ...