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 14 Jul 2026] Title:Human-Centric Grasp State Assessment: Toward Transferring Subjective Evaluation to Robots View a PDF of the paper titled Human-Centric Grasp State Assessment: Toward Transferring Subjective Evaluation to Robots, by Ryohei Kobayashi and 4 other authors View PDF HTML (experimental) Abstract:We propose a framework that transfers tacit human subjective criteria to robotic systems for the appropriate grasping of deformable objects. Achieving such behavior is challenging because a semantic gap exists between qualitative human expectations and quantitative robotic measurements. Conventional deep learning approaches for bridging this gap also require prohibitive amounts of manually annotated data for each newly encountered object. To address these challenges, our framework integrates a Vision-Language Model (VLM)-based semi-automated supervisor generator with a lightweight grasp state predictor, using a minimal set of human-annotated trials as contextual anchors to propagate subjective criteria to unannotated data. The prediction model then enables rapid online adaptation by sequentially estimating the grasp state from time-series tactile and grasping force measurements. Through experiments on three representative deformable objects and a human evaluation study with 25 participants, we demonstrate the feasibility of the proposed framework for adjusting grasping force according to human-perceived grasp appropriateness in the evaluated task setting. Comments: 8 pages, accepted as a conference paper for IEEE International Conference on Development and Learning (ICDL2026) Subjects: Robotics (cs.RO) Cite as: arXiv:2609.17540 [cs.RO] (or arXiv:2609.17540v1 [cs.RO] for this version) https://doi.org/10.48550/arXiv.2609.17540 arXiv-issued DOI via DataCite Submission history From: Yuga Yano [view email] [v1] Tue, 14 Jul 2026 15:51:42 UTC (6,037 KB) Full-text links: Access Paper: View a PDF of the paper titled Human-Centric Grasp State Assessment: Toward Transferring Subjective Evaluation to Robots, by Ryohei Kobayashi and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.RO new | recent | 2026-09 Change to browse by: cs 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?)