以人为中心的抓取状态评估：将主观评价迁移至机器人
Human-Centric Grasp State Assessment: Toward Transferring Subjective Evaluation to Robots
查看机构详情
- Kyushu Institute of Technology（九州工业大学）
- Research Center for Neuromorphic AI Hardware, Kyushu Institute of Technology（九州工业大学神经形态AI硬件研究中心）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
提出一个集成VLM半自动监督生成器与轻量级预测器的框架，利用少量人工标注将主观抓取标准迁移至机器人，实现对可变形物体的自适应抓取力调整。
中文摘要 AI 辅助
我们提出一个框架，将人类隐性主观标准迁移至机器人系统，以实现对可变形物体的适当抓取。实现这种行为具有挑战性，因为定性的人类期望与定量的机器人测量之间存在语义鸿沟。用于弥合这一鸿沟的常规深度学习方法，对于每个新遇到的物体，也需要大量的人工标注数据。为应对这些挑战，我们的框架将基于视觉语言模型（VLM）的半自动监督生成器与轻量级抓取状态预测器相结合，利用最少的人工标注试验作为上下文锚点，将主观标准传播至未标注数据。随后，预测模型通过从时间序列的触觉和抓取力测量中顺序估计抓取状态，实现快速在线自适应。通过对三个代表性可变形物体的实验以及一项包含25名参与者的人类评估研究，我们证明了所提框架在所评估任务设置中，根据人类感知的抓取适切性调整抓取力的可行性。
英文摘要
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.
