Planning with Task-Oriented Knowledge Acquisition for a Service Robot

We propose a framework for a service robot to behave intelligently in domains that contain incomplete information, underspecified goals and dynamic change. Human robot interaction (HRI), sensing actions and physical actions are uniformly formalized in action language BC. An answer set solver is called to generate plans that guide the robot to acquire task-oriented knowledge and execute actions to achieve its goal, including interacting with human to gather information and sensing the environment to help motion planning. By continuously interpreting and grounding useful sensing information, robot is able to use contingent knowledge to adapt to unexpected changes and faults. We evaluate the approach on service robot Ke-Jia that serves drink to guests, a testing benchmark for general-purpose service robot proposed by [email protected] competition.

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