The increasing presence of robots in industries has not gone unnoticed. Large industrial players have incorporated them into their production lines, but smaller companies hesitate due to high initial costs and the lack of programming expertise. In this work we introduce a framework that combines two disciplines, Programming by Demonstration and Automated Planning, to allow users without any programming knowledge to program a robot. The user teaches the robot atomic actions together with their semantic meaning and represents them in terms of preconditions and effects. Using these atomic actions the robot can generate action sequences autonomously to reach any goal given by the user. We evaluated the usability of our framework in terms of user experiments with a Baxter Research Robot and showed that it is well-adapted to users without any programming experience.
Our work uses the policy derivation technique of plans, where the policy is represented as a sequence of actions leading from an initial state to a final goal state. Actions are defined in terms of preconditions, i.e. a state of the world that must be attained in order to execute the action, and effects, i.e. the expected state resulting from the action execution. Existing implementations of plans learn action sequences to achieve a single goal starting from different initial states @cite_6 , but do not learn different action models that can be applied independently. Our approach teaches the robot independent action models that can be used by automated planning techniques to reach any given goal.
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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 identification 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 different initial state, the system re-instantiates and executes the task plan to accomplish an equivalent goal. The effectiveness of our method is supported by experimental results with block assembly tasks. >