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Autonomous Learning of Task Skills and Human Intention for Enhancing Human Trust of Robot Systems.
| 题名： | Autonomous Learning of Task Skills and Human Intention for Enhancing Human Trust of Robot Systems. |
| 作者： | Suh, I. H. |
| 关键词： | Robots, Motion, Machine learning, Interactions, Hidden markov models, Human machine trust, Autonomous learning, Mpw (motion primitive words), Motion significance, Human intention, Ws (workstreams), Motion trajectories, Motion grammar, Motion complexity, Hmm (hidden markov models) |
| 摘要： | The PI's team proposed a framework in which a robot learns task skills enough to understand and execute a given task as reliably as possible. Motion significance and motion complexity measures that can be used to segment motion trajectories and assign MPWs (Motion Primitive Words) were implemented to accomplish this. The PI's team developed a method of regression of MPWs preserving motion significance to adapt, improve and/or reuse them. They also devised a method of representing pre- and post-conditions by analyzing motion significance of all possible object-object motion pairs and object-robot motion pairs. It was expected that a novel task skill can be acquired by compositionality of MPWs using working conditions represented by PDDL, and a robot can explain why the robot has to deploy a MPW under its current situation. This enhances human trust of robot systems. To show the validity of this framework, the team performed several experiments for learning task skills and social interaction skills for robots and digital avatars. |
| 报告类型： | 科技报告 |
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