{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T18:51:18Z","timestamp":1755802278561,"version":"3.44.0"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783032020178"},{"type":"electronic","value":"9783032020185"}],"license":[{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:00:00Z","timestamp":1755820800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-02018-5_34","type":"book-chapter","created":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T10:16:19Z","timestamp":1755771379000},"page":"472-484","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Architectural Mitigation of\u00a0Control AI Risk Factors for\u00a0Safe Human-Robot-Collaboration"],"prefix":"10.1007","author":[{"given":"Andreas","family":"Kreutz","sequence":"first","affiliation":[]},{"given":"Ren\u00e9","family":"Beck","sequence":"additional","affiliation":[]},{"given":"Gereon","family":"Weiss","sequence":"additional","affiliation":[]},{"given":"Satoshi","family":"Otsuka","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,8,22]]},"reference":[{"key":"34_CR1","doi-asserted-by":"crossref","unstructured":"Abbas, A.N., et al.: Safety-Driven Deep Reinforcement Learning Framework for Cobots: A Sim2Real Approach (2024)","DOI":"10.1109\/CoDIT62066.2024.10708345"},{"key":"34_CR2","doi-asserted-by":"crossref","unstructured":"Brunke, L., et al.: Safe learning in robotics: from learning-based control to safe reinforcement learning (2021). arXiv:2108.06266","DOI":"10.1146\/annurev-control-042920-020211"},{"key":"34_CR3","doi-asserted-by":"publisher","unstructured":"Chemweno, P., Pintelon, L., Decre, W.: Orienting safety assurance with outcomes of hazard analysis and risk assessment: a review of the ISO 15066 standard for collaborative robot systems. 129, 104832. https:\/\/doi.org\/10.1016\/j.ssci.2020.104832. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0925753520302290","DOI":"10.1016\/j.ssci.2020.104832"},{"key":"34_CR4","unstructured":"Cyberbotics Ltd.: Webots. https:\/\/cyberbotics.com\/. Accessed 23 Apr 2025"},{"key":"34_CR5","unstructured":"DIN EN ISO 10218-1:2021: Robotik - Sicherheitsanforderungen - Teil 1: Industrieroboter (2021). DIN Media GmbH"},{"key":"34_CR6","unstructured":"DIN EN ISO 12100:2025: Safety of machinery - general principles for design - risk assessment and risk reduction (2025). Beuth Verlag GmbH"},{"key":"34_CR7","unstructured":"DIN ISO\/TS 15066:2017: Robots and robotic devices - Collaborative robots (2017). Beuth Verlag GmbH"},{"key":"34_CR8","doi-asserted-by":"crossref","unstructured":"Ferreira, R.S., Gu\u00e9rin, J., Delmas, K., Guiochet, J., Waeselynck, H.: Safety Monitoring of Machine Learning Perception Functions: A Survey (2024)","DOI":"10.1111\/coin.70032"},{"key":"34_CR9","unstructured":"Garc\u0131a, J., Fernandez, F.: A comprehensive survey on safe reinforcement learning. 6(1) (2015)"},{"key":"34_CR10","unstructured":"Gu, S., et al.: A review of safe reinforcement learning: methods, theory and applications (2024). arxiv:2205.10330"},{"key":"34_CR11","doi-asserted-by":"crossref","unstructured":"Haider, T., Roscher, K., Herd, B., Schmoeller\u00a0Roza, F., Burton, S.: Can you trust your Agent? The effect of out-of-distribution detection on the safety of reinforcement learning systems. In: Proceedings of the 39th ACM\/SIGAPP Symposium on Applied Computing. ACM, Avila, Spain (2024)","DOI":"10.1145\/3605098.3635931"},{"key":"34_CR12","unstructured":"Haider, T., Roscher, K., Schmoeller\u00a0Roza, F., Guennemann, S.: Out-of-Distribution Detection for Reinforcement Learning Agents with Probabilistic Dynamics Models (2023)"},{"key":"34_CR13","unstructured":"ISO 31000:2018: Risk management - Guidelines (2018). International Organization for Standardization"},{"key":"34_CR14","unstructured":"ISO\/IEC 5338:2023: Information technology - Artificial intelligence - AI system life cycle processes (2023). International Organization for Standardization"},{"key":"34_CR15","unstructured":"ISO\/IEC TR 5469:2024: Artificial intelligence - Functional safety and AI systems (2024). International Organization for Standardization"},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Kreutz, A., Weiss, G., Trapp, M.: Automatic deduction of the impact of context variability on system safety goals. In: 2024 19th European Dependable Computing Conference (EDCC) (2024)","DOI":"10.1109\/EDCC61798.2024.00015"},{"key":"34_CR17","doi-asserted-by":"crossref","unstructured":"Kreutz, A., Weiss, G., Trapp, M.: Modeling safe adaptation spaces for self-adaptive systems using contextual safety concept trees. In: 2025 IEEE\/ACM 20th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS) (2025)","DOI":"10.1109\/SEAMS66627.2025.00018"},{"key":"34_CR18","unstructured":"Kuznetsov, A., Shvechikov, P., Grishin, A., Vetrov, D.: Controlling overestimation bias with truncated mixture of continuous distributional quantile critics. In: International Conference on Machine Learning, vol. 5556\u20135566 (2020)"},{"key":"34_CR19","doi-asserted-by":"crossref","unstructured":"Ladosz, P., Weng, L., Kim, M., Oh, H.: Exploration in deep reinforcement learning: a survey. 85 (2022). arxiv:2205.00824","DOI":"10.1016\/j.inffus.2022.03.003"},{"key":"34_CR20","doi-asserted-by":"crossref","unstructured":"Mnih, V., et al.: Human-level control through deep reinforcement learning 518(7540) (2015). https:\/\/www.nature.com\/articles\/nature14236","DOI":"10.1038\/nature14236"},{"key":"34_CR21","doi-asserted-by":"crossref","unstructured":"Mukherjee, D., Gupta, K., Chang, L.H., Najjaran, H.: A survey of robot learning strategies for human-robot collaboration in industrial settings. Robot. Comput.-Integr. Manuf. 73 (2022)","DOI":"10.1016\/j.rcim.2021.102231"},{"key":"34_CR22","doi-asserted-by":"crossref","unstructured":"Salvi, A., Weiss, G., Trapp, M.: Adaptively managing reliability of machine learning perception under changing operating conditions. In: 2023 IEEE\/ACM 18th Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS). IEEE (2023)","DOI":"10.1109\/SEAMS59076.2023.00019"},{"key":"34_CR23","doi-asserted-by":"crossref","unstructured":"Schnitzer, R., Hapfelmeier, A., Gaube, S., Zillner, S.: AI Hazard Management: a framework for the systematic management of root causes for AI risks (2024)","DOI":"10.1007\/978-981-99-9836-4_27"},{"key":"34_CR24","doi-asserted-by":"crossref","unstructured":"Schnitzer, R., Kilian, L., Roessner, S., Theodorou, K., Zillner, S.: Landscape of AI safety concerns \u2013 a methodology to support safety assurance for AI-based autonomous systems (2024)","DOI":"10.1109\/ICSRS63046.2024.10927556"},{"key":"34_CR25","unstructured":"Siemens, A.G., et al.: Safe.trAIn Project Website. https:\/\/safetrain-projekt.de\/. Accessed 23 Apr 2025"},{"key":"34_CR26","doi-asserted-by":"crossref","unstructured":"Thumm, J., Althoff, M.: Provably safe deep reinforcement learning for robotic manipulation in human environments. In: 2022 International Conference on Robotics and Automation (ICRA). IEEE (2022)","DOI":"10.1109\/ICRA46639.2022.9811698"},{"key":"34_CR27","unstructured":"Trapp, M., Herd, B., Rank, B.: Utilizing potentially unsafe capabilities in safety-critical systems. In: CARS 2025 9th International Workshop on Critical Automotive Applications: Robustness & Safety (2025)"},{"key":"34_CR28","doi-asserted-by":"crossref","unstructured":"Wang, L., et al.: Symbiotic human-robot collaborative assembly. CIRP Ann. 68(2) (2019)","DOI":"10.1016\/j.cirp.2019.05.002"},{"key":"34_CR29","doi-asserted-by":"crossref","unstructured":"Weiss, G., Schleiss, P., Schneider, D., Trapp, M.: Towards integrating undependable self-adaptive systems in safety-critical environments. In: Proceedings of the 13th International Conference on Software Engineering for Adaptive and Self-Managing Systems. ACM, Gothenburg, Sweden (2018)","DOI":"10.1145\/3194133.3194157"},{"key":"34_CR30","doi-asserted-by":"crossref","unstructured":"Weiss, G., Zeller, M., Schoenhaar, H., Drabek, C., Kreutz, A.: Approach for argumenting safety on basis of an operational design domain. In: Proceedings of the IEEE\/ACM 3rd International Conference on AI Engineering - Software Engineering for AI. ACM (2024)","DOI":"10.1145\/3644815.3644944"},{"key":"34_CR31","doi-asserted-by":"crossref","unstructured":"Willers, O., Sudholt, S., Raafatnia, S., Abrecht, S.: Safety Concerns and Mitigation Approaches Regarding the Use of Deep Learning in Safety-Critical Perception Tasks (2020)","DOI":"10.1007\/978-3-030-55583-2_25"},{"key":"34_CR32","unstructured":"Xu, M., et al.: Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability (2022)"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Zolfagharian, A., Abdellatif, M., Briand, L.C.: SMARLA: a safety monitoring approach for deep reinforcement learning agents. IEEE Trans. Softw. Eng. 51(1) (2025)","DOI":"10.1109\/TSE.2024.3491496"}],"container-title":["Lecture Notes in Computer Science","Computer Safety, Reliability, and Security. SAFECOMP 2025 Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02018-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T10:16:31Z","timestamp":1755771391000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02018-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,22]]},"ISBN":["9783032020178","9783032020185"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02018-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2025,8,22]]},"assertion":[{"value":"22 August 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SAFECOMP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Safety, Reliability, and Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stockholm","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"safecomp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/safecomp2025.se\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}