{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:53:44Z","timestamp":1775746424589,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":32,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819584079","type":"print"},{"value":"9789819584086","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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\/978-981-95-8408-6_13","type":"book-chapter","created":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:09:37Z","timestamp":1775743777000},"page":"243-262","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RoboClarify: Clarifying Ambiguous Instructions Through Scenario-Guided Risk Assessment for\u00a0Home Embodied Agents"],"prefix":"10.1007","author":[{"given":"Ying","family":"Liang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhihui","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaowen","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhen","family":"Dai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongle","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Limin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,4,10]]},"reference":[{"key":"13_CR1","unstructured":"Achiam, J., et\u00a0al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023"},{"key":"13_CR2","unstructured":"Explosion AI. spacy: Industrial-strength natural language processing in python, (2017)"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Barringer, H., Goldberg, A., Havelund, K., Sen, K.: Rule-based runtime verification. In: International Workshop on Verification, Model Checking, and Abstract Interpretation, pp. 44\u201357. Springer (2004)","DOI":"10.1007\/978-3-540-24622-0_5"},{"key":"13_CR4","unstructured":"Alibaba Cloud. Qwen language model. https:\/\/github.com\/QwenLM (2023). Accessed 15 July 2025"},{"key":"13_CR5","unstructured":"Dai, J., et al.: Safe rlhf: Safe reinforcement learning from human feedback. arXiv preprint arXiv:2310.12773 (2023)"},{"key":"13_CR6","unstructured":"Dalal, M., Chiruvolu, T., Chaplot, D. and Salakhutdinov, R.: Plan-seq-learn: Language model guided RL for solving long horizon robotics tasks. arXiv preprint arXiv:2405.01534 (2024)"},{"key":"13_CR7","unstructured":"Google DeepMind. Gemini 2.5. https:\/\/deepmind.google\/technologies\/gemini (2025). Accessed 15 July 2025"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Grigorev, D.S., Kovalev, A.K., Panov, A.I.:. Common sense plan verification with large language models. In: International Conference on Hybrid Artificial Intelligence Systems, pp. 224\u2013236. Springer, (2024)","DOI":"10.1007\/978-3-031-74186-9_19"},{"key":"13_CR9","unstructured":"Huang, W., Pan, T., Ye, Y.: Graphormer-guided task planning: Beyond static rules with LLM safety perception. arXiv preprint arXiv:2503.06866, 2025"},{"issue":"19","key":"13_CR10","doi-asserted-by":"publisher","first-page":"8868","DOI":"10.3390\/app14198868","volume":"14","author":"H Jeong","year":"2024","unstructured":"Jeong, H., Lee, H., Kim, C., Shin, S.: A survey of robot intelligence with large language models. Appl. Sci. 14(19), 8868 (2024)","journal-title":"Appl. Sci."},{"key":"13_CR11","unstructured":"Jiang, Y., et al.: Behavior robot suite: Streamlining real-world whole-body manipulation for everyday household activities. arXiv preprint arXiv:2503.05652 (2025)"},{"issue":"5","key":"13_CR12","doi-asserted-by":"publisher","first-page":"1091","DOI":"10.1007\/s11370-024-00550-5","volume":"17","author":"Y Kim","year":"2024","unstructured":"Kim, Y., Kim, D., Choi, J., Park, J., Nayoung, O., Park, D.: A survey on integration of large language models with intelligent robots. Intel. Serv. Robot. 17(5), 1091\u20131107 (2024)","journal-title":"Intel. Serv. Robot."},{"key":"13_CR13","unstructured":"Kolve, E., et\u00a0al.: Ai2-thor: An interactive 3D environment for visual ai. arXiv preprint arXiv:1712.05474 (2017)"},{"key":"13_CR14","unstructured":"Tencent\u00a0AI Lab. Hunyuan (yuanbao) model. https:\/\/hunyuan.tencent.com\/ (2023). Accessed 15 July 2025"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Li, S., et al.: Safe planner: empowering safety awareness in large pre-trained models for robot task planning. In Proceedings of the AAAI Conference on Artificial Intelligence, vol. 39, 14619\u201314627 (2025)","DOI":"10.1609\/aaai.v39i14.33602"},{"key":"13_CR16","unstructured":"Liu, A., et\u00a0al. Deepseek-v2: a strong, economical, and efficient mixture-of-experts language model. arXiv preprint arXiv:2405.04434 (2024)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Liu, X., Li, x., Guo, D., Tan, S., Liu, H., Sun, F: Embodied multi-agent task planning from ambiguous instruction. In:Robotics: Science and Systems, pp. 1\u201314D (2022)","DOI":"10.15607\/RSS.2022.XVIII.032"},{"key":"13_CR18","unstructured":"OpenAI. Chatgpt. https:\/\/chat.openai.com (2023). Accessed 15 July 2025"},{"key":"13_CR19","unstructured":"OpenAI. Gpt-4o mini: advancing cost-efficient intelligence. https:\/\/openai.com\/index\/gpt-4o-mini-advancing-cost-efficient-intelligence\/ (2024). Accessed 15 July 2025"},{"key":"13_CR20","unstructured":"Ramrakhya, R., Chang, M., Puig, X., Desai, R., Kira, Z., Mottaghi, R.: Grounding multimodal LLMs to embodied agents that ask for help with reinforcement learning. arXiv preprint arXiv:2504.00907 (2025)"},{"key":"13_CR21","doi-asserted-by":"crossref","unstructured":"Reinbacher, T., Rozier, K.Y., Schumann, J.: Temporal-logic based runtime observer pairs for system health management of real-time systems. In: International Conference on Tools and Algorithms for the Construction and Analysis of Systems, pp. 357\u2013372. Springer (2014)","DOI":"10.1007\/978-3-642-54862-8_24"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Schumann, J., Moosbrugger, P., Rozier, K.Y.: R2u2: monitoring and diagnosis of security threats for unmanned aerial systems. In: Runtime Verification: 6th International Conference, RV 2015, Vienna, Austria, September 22-25, 2015. Proceedings, pp. 233\u2013249. Springer (2015)","DOI":"10.1007\/978-3-319-23820-3_15"},{"key":"13_CR23","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.entcs.2004.01.029","volume":"113","author":"P Thati","year":"2005","unstructured":"Thati, P., Ro\u015fu, G.: Monitoring algorithms for metric temporal logic specifications. Electron. Notes Theor. Comput. Sci. 113, 145\u2013162 (2005)","journal-title":"Electron. Notes Theor. Comput. Sci."},{"key":"13_CR24","unstructured":"Wang, H., Poskitt, C.M., Sun, J.: Agentspec: Customizable runtime enforcement for safe and reliable LLM agents. arXiv preprint arXiv:2503.18666 (2025)"},{"key":"13_CR25","unstructured":"Wei, J., et\u00a0al.: Emergent abilities of large language models. arXiv preprint arXiv:2206.07682 (2022)"},{"issue":"8","key":"13_CR26","doi-asserted-by":"publisher","first-page":"1087","DOI":"10.1007\/s10514-023-10139-z","volume":"47","author":"W Jimmy","year":"2023","unstructured":"Jimmy, W., et al.: Tidybot: personalized robot assistance with large language models. Auton. Robot. 47(8), 1087\u20131102 (2023)","journal-title":"Auton. Robot."},{"key":"13_CR27","doi-asserted-by":"crossref","unstructured":"Wu, Y., et al.: Selp: Generating safe and efficient task plans for robot agents with large language models. arXiv preprint arXiv:2409.19471 (2024)","DOI":"10.1109\/ICRA55743.2025.11128420"},{"key":"13_CR28","unstructured":"Yin, S., et al.: Safeagentbench: a benchmark for safe task planning of embodied LLM agents. arXiv preprint arXiv:2412.13178 (2024)"},{"key":"13_CR29","unstructured":"Zhang, B., et al.: Safevla: towards safety alignment of vision-language-action model via constrained learning. arXiv preprint arXiv:2503.03480 (2025)"},{"key":"13_CR30","unstructured":"Zhang, W., Kong, X., Braunl, T., Hong, J.B.: Safeembodai: a safety framework for mobile robots in embodied ai systems. arXiv preprint arXiv:2409.01630 (2024)"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Lee, W.S., Hsu, D.: Large language models as commonsense knowledge for large-scale task planning. Adv. Neural Inform. Process. Syst. 36, 31967\u201331987 (2023)","DOI":"10.52202\/075280-1387"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, E., et al.: Code-as-monitor: constraint-aware visual programming for reactive and proactive robotic failure detection. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 6919\u20136929 (2025)","DOI":"10.1109\/CVPR52734.2025.00649"}],"container-title":["Lecture Notes in Computer Science","Algorithms and Architectures for Parallel Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-95-8408-6_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:09:46Z","timestamp":1775743786000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-95-8408-6_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819584079","9789819584086"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-981-95-8408-6_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 April 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"ICA3PP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Algorithms and Architectures for Parallel Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Zhengzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","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":"30 October 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 November 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ica3pp2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ieee-cybermatics.org\/2025\/ica3pp\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}