待翻譯：Multimodal-Language-Model-Driven Interaction and Companionship for Service Robots in Elderly-Care Facilities
AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.21387v1 Announce Type: new Abstract: Service robots are increasingly deployed in elderly-care facilities to alleviate caregiver workload and enhance the quality of daily care. However, most existing studies focus on isolated service functions and lack integrated capabilities for continuous companionship, natural interaction, and safety monitoring. In this paper, we present an intelligent companion robot system that unifies active visual human-following, real-time LLM-driven speech interaction for intent understanding and task execution, and VLM-based safety monitoring for fall detection and abnormal posture assessment. The perception layer ensures robust human tracking and uses an active gimbal to maintain the user in view during occlusions or abrupt movements. At the interaction layer, a Large Language Model interprets spoken requests and maps them to robot actions, enabling escorting and semantic navigation. Simultaneously, a VLM-based safety agent continuously analyzes visual observations to detect fall-related or abnormal postures and triggers emergency responses when necessary. Experimental results demonstrate the system's ability to reliably follow and interact with humans, while effectively detecting potential falls to ensure user safety.
AI 服務暫時不可用，以下為來源正文，待恢復後補全翻譯。
--> [Submitted on 22 Jul 2026] Title:Multimodal-Language-Model-Driven Interaction and Companionship for Service Robots in Elderly-Care Facilities View a PDF of the paper titled Multimodal-Language-Model-Driven Interaction and Companionship for Service Robots in Elderly-Care Facilities, by Ching-Chieh Liu and 1 other authors View PDF HTML (experimental) Abstract:Service robots are increasingly deployed in elderly-care facilities to alleviate caregiver workload and enhance the quality of daily care. However, most existing studies focus on isolated service functions and lack integrated capabilities for continuous companionship, natural interaction, and safety monitoring. In this paper, we present an intelligent companion robot system that unifies active visual human-following, real-time LLM-driven speech interaction for intent understanding and task execution, and VLM-based safety monitoring for fall detection and abnormal posture assessment. The perception layer ensures robust human tracking and uses an active gimbal to maintain the user in view during occlusions or abrupt movements. At the interaction layer, a Large Language Model interprets spoken requests and maps them to robot actions, enabling escorting and semantic navigation. Simultaneously, a VLM-based safety agent continuously analyzes visual observations to detect fall-related or abnormal postures and triggers emergency responses when necessary. Experimental results demonstrate the system's ability to reliably follow and interact with humans, while effectively detecting potential falls to ensure user safety. Comments: Accepted to the 2026 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM) Subjects: Robotics (cs.RO); Systems and Control (eess.SY) Cite as: arXiv:2608.21387 [cs.RO] (or arXiv:2608.21387v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2608.21387 arXiv-issued DOI via DataCite Submission history From: Cong-Thanh Vu [view email] [v1] Wed, 22 Jul 2026 05:27:14 UTC (1,625 KB) Full-text links: Access Paper: View a PDF of the paper titled Multimodal-Language-Model-Driven Interaction and Companionship for Service Robots in Elderly-Care Facilities, by Ching-Chieh Liu and 1 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-08 Change to browse by: cs cs.SY eess eess.SY References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)