首页|New Findings from Imperial College London in Androids Provides New Insights (A F ramework for Trust-related Knowledge Transfer In Human-robot Interaction)
New Findings from Imperial College London in Androids Provides New Insights (A F ramework for Trust-related Knowledge Transfer In Human-robot Interaction)
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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."
LondonUnited KingdomEuropeAndroidsEmerging TechnologiesHuman-Robot InteractionMachine LearningRobotRobot icsImperial College London
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