Abstract| A novel task instruction method for future intelligent robots is presented. In our method, a robot learns reusable task plans by watching a human perform assembly tasks. Functional units and working algorithms for visual recognition and analysis of human action sequences are presented. The overall system is model based and integrated at the symbolic level. Temporal segmentation of a continuous task performance into meaningful units and identication of each operation is processed in real time by concurrent recognition processes under active attention control. Dependency among assembly operations in the recognized action sequence is analyzed, which results in a hierarchical task plan describing the higher level structure of the task. In another workspace with a dierent initial state, the system re-instantiates and executes the task plan to accomplish an equivalent goal. The eectiveness of our method is supported by experimental results with block assembly tasks.
In REF , a robot agent watches a human teacher performing a simple assembly task in a tabletop environment.
Learning by Watching: Extracting Reusable Task Knowledge from Visual Observation of Human Performance
IEEE Transactions on Robotics and Automation
Computer Science