摘要
由新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-关于机器人的详细数据-机器人h已经呈现。根据英国伦敦的新闻报道，b y NewsRx编辑，研究表明：“值得信赖的人-机器人互动(HRI)du ring activities of dairy living(ADL)为辅助机器人提供了一个有趣而富有挑战性的do main，”特别是由于人类参与者对辅助机器人的信任水平的估计方法仍然处于研究阶段。信任是一个多面概念，受机器人和人类之间的互动影响，并且除其他因素外，取决于机器人的功能历史、任务和环境状态。这项研究的财政支持来自英国研究与创新(UKRI)。我们的新闻记者从罗恩登帝国理工学院的研究中得到一句话：“在本文中，我们关注的是信任转移的挑战，”在新的协作TA SK的信任级别推断中，是否可以考虑来自先前协作任务的交互经验。这有可能避免在每个新的情况下从头重新计算信任级别。这里的关键挑战是自动评估原始和新情况之间的相似性，然后利用以前对各种物体和任务的经验，使机器人的行为适应新的情况。我们测量了知识图(KGs)中概念之间的语义相似性，并根据个性化的交互历史来调整机器人的动作适应特定用户。这些动作被建立起来，然后在执行之前使用几何运动规划器来生成新情况下可行的轨迹。该框架已经在不同厨房场景下的人-机器人交接任务中进行了实验测试。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Data detailed on Robotics-Androids h ave been presented. According to news reporting out of London, United Kingdom, b y NewsRx editors, research stated, "Trustworthy humanrobot interaction (HRI) du ring activities of daily living (ADL) presents an interesting and challenging do main for assistive robots, particularly since methods for estimating the trust l evel of a human participant towards the assistive robot are still in their infan cy. Trust is a multifaced concept which is affected by the interactions between the robot and the human, and depends, among other factors, on the history of the robot's functionality, the task and the environmental state." Financial support for this research came from UK Research & Innova tion (UKRI). Our news journalists obtained a quote from the research from Imperial College Lo ndon, "In this paper, we are concerned with the challenge of trust transfer, i.e . whether experiences from interactions on a previous collaborative task can be taken into consideration in the trust level inference for a new collaborative ta sk. This has the potential of avoiding re-computing trust levels from scratch fo r every new situation.The key challenge here is to automatically evaluate the s imilarity between the original and the novel situation, then adapt the robot's b ehaviour to the novel situation using previous experience with various objects a nd tasks. To achieve this, we measure the semantic similarity between concepts i n knowledge graphs (KGs) and adapt the robot's actions towards a specific user b ased on personalised interaction histories. These actions are grounded and then verified before execution using a geometric motion planner to generate feasible trajectories in novel situations. This framework has been experimentally tested in human-robot handover tasks in different kitchen scene contexts."
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