@inproceedings {icaps16-106,
    track    = {​​Robotics Track},
    title    = {Experience-Based Robot Task Learning and Planning with Goal Inference},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/13175},
    author   = {Vahid Mokhtari and  Luis Seabra Lopes and  Armando Jose Pinho},
    abstract = {Learning and deliberation are required to endow a robotwith the capabilities to acquire knowledge, perform a variety of tasks and interactions, and adapt to open-ended environments. This paper explores the notion of experience-based planning domains (EBPDs) for task-level learning and planning in robotics. EBPDs rely on methods for a robot to: (i) obtain robot activity experiences from the robot's performance; (ii) conceptualize each experience to a task model called activity schema; and (iii) exploit the learned activity schemata to make plans in similar situations. Experiences are episodic descriptions of plan-based robot activities including environment perception, sequences of applied actions and achieved tasks. The conceptualization approach integrates different techniques including deductive generalization, abstraction and feature extraction to learn activity schemata. A high-level task planner was developed to find a solution for a similar task by following an activity schema. In this paper, we extend our previous approach by integrating goal inference capabilities. The proposed approach is illustrated in a restaurant environment where a service robot learns how to carry out complex tasks.},
    keywords = {planning domain representations for robotics applications,acquisition of planning models for robotics,learning action and task models,integrated planning and execution in robotic architectures,real-world planning applications for autonomous robots}
}

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