Planning with Task-oriented Knowledge Acquisition for A Service Robot

Planning with Task-oriented Knowledge Acquisition for A Service Robot

Kai Chen1, Fangkai Yang2 and Xiaoping Chen1

1School of Computer Science and Technology, University of Science and Technology of China
96 Jinzhai Rd, Hefei, Anhui, 200027, China
[email protected], [email protected]
2Katy Drilling Software Center, Schlumberger Software Technology, Schlumberger Ltd.
23500 Colonial Parkway, Katy, TX, 77493, USA
[email protected]



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 Kejia that serves drink to guests, a testing benchmark for general purpose service robot proposed by Robocup@Home competition.