Inferring robot goals from violations of semantic knowledge

A growing body of literature shows that endowing a mobile robot with semantic knowledge and with the ability to reason from this knowledge can greatly increase its capabilities. In this paper, we present a novel use of semantic knowledge, to encode information about how things should be, i.e. norms, and to enable the robot to infer deviations from these norms in order to generate goals to correct these deviations. For instance, if a robot has semantic knowledge that perishable items must be kept in a refrigerator, and it observes a bottle of milk on a table, this robot will generate the goal to bring that bottle into a refrigerator. The key move is to properly encode norms in an ontology so that each norm violation results in a detectable inconsistency. A goal is then generated to bring the world back in a consistent state, and a planner is used to transform this goal into actions. Our approach provides a mobile robot with a limited form of goal autonomy: the ability to derive its own goals to pursue generic aims. We illustrate our approach in a full mobile robot system that integrates a semantic map, a knowledge representation and reasoning system, a task planner, and standard perception and navigation routines.

[1]  Pierre Lison,et al.  Self-Understanding and Self-Extension: A Systems and Representational Approach , 2010, IEEE Transactions on Autonomous Mental Development.

[2]  C. Stachniss,et al.  Semantic Modeling of Places using Objects , 2008 .

[3]  Sven Wachsmuth,et al.  A Computational Model for the Alignment of Hierarchical Scene Representations in Human-Robot Interaction , 2009, IJCAI.

[4]  Cipriano Galindo,et al.  Multiple Abstraction Hierarchies for Mobile Robot Operation in Large Environments , 2007 .

[5]  Alessandro Saffiotti,et al.  Using semantic knowledge in robotics , 2008, Robotics Auton. Syst..

[6]  Alessandro Saffiotti,et al.  Some notes on the use of hybrid maps for mobile robots , 2004 .

[7]  Masahiro Fujita,et al.  An ethological and emotional basis for human-robot interaction , 2003, Robotics Auton. Syst..

[8]  Christopher S. Martin.  Agent Autonomy: Specification, Measurement, and Dynamic Adjustment , 1999 .

[9]  Derek Long,et al.  Alarms: An Implementation of Motivated Agency , 1995, ATAL.

[10]  Diego Calvanese,et al.  The Description Logic Handbook: Theory, Implementation, and Applications , 2003, Description Logic Handbook.

[11]  Alessandro Saffiotti,et al.  Robots that Change Their World: Inferring Goals from Semantic Knowledge , 2011, ECMR.

[12]  Roland Siegwart,et al.  Cognitive maps for mobile robots - an object based approach , 2007, Robotics Auton. Syst..

[13]  Alessandro Saffiotti,et al.  Autonomous functional configuration of a network robot system , 2008, Robotics Auton. Syst..

[14]  R. Conte,et al.  Cognitive and social action , 1995 .

[15]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[16]  Joachim Hertzberg,et al.  Towards semantic maps for mobile robots , 2008, Robotics Auton. Syst..

[17]  Wolfram Burgard,et al.  From labels to semantics: an integrated system for conceptual spatial representations of indoor environments for mobile robots , 2007 .

[18]  Austin Tate,et al.  Interacting Goals And Their Use , 1975, IJCAI.

[19]  Moshe Sipper,et al.  On the Origin of Environments by Means of Natural Selection , 2001, AI Mag..

[20]  Kevin M. Passino,et al.  Ontologically Controlled Autonomous Systems: Principles, Operations, and Architecture , 1997 .

[21]  Thomas Eiter,et al.  Maintenance goals of agents in a dynamic environment: Formulation and policy construction , 2008, Artif. Intell..

[22]  Alessandro Saffiotti,et al.  Robot task planning using semantic maps , 2008, Robotics Auton. Syst..

[23]  Paolo Traverso,et al.  Automated Planning: Theory & Practice , 2004 .

[24]  Alessandro Saffiotti,et al.  Semantic Norms for Mobile Robots: When the End Does Not Justify the Means , 2012, SyRoCo.

[25]  Guilin Qi,et al.  A revision-based approach to handling inconsistency in description logics , 2006, Artificial Intelligence Review.

[26]  Kristian J. Hammond,et al.  The Stabilization of Environments , 1995, Artif. Intell..

[27]  Alessandro Saffiotti,et al.  Seamless integration of robots and tiny embedded devices in a PEIS-Ecology , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Drew McDermott,et al.  The 1998 AI Planning Systems Competition , 2000, AI Mag..

[29]  Alessandro Saffiotti,et al.  The PEIS-Ecology project: Vision and results , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Grzegorz Cielniak,et al.  Toward an object-based semantic memory for long-term operation of mobile service robots , 2010 .

[31]  Dana Nau,et al.  A General Approach to Synthesize Problem-Specific Planners , 2003 .

[32]  Moritz Tenorth,et al.  RoboEarth - A World Wide Web for Robots , 2011, ICRA 2011.

[33]  Richard Booth,et al.  Knowledge Integration for Description Logics , 2005, AAAI.

[34]  Yarden Katz,et al.  Pellet: A practical OWL-DL reasoner , 2007, J. Web Semant..

[35]  Alois Zoitl,et al.  Ontology-based fault diagnosis for industrial control applications , 2010, 2010 IEEE 15th Conference on Emerging Technologies & Factory Automation (ETFA 2010).

[36]  Alessandro Saffiotti,et al.  Monitoring the execution of robot plans using semantic knowledge , 2008, Robotics Auton. Syst..

[37]  Richard D. Braatz,et al.  Fault Detection and Diagnosis in Industrial Systems , 2001 .

[38]  Mehdi Dastani,et al.  What Is a Normative Goal?: Towards Goal-Based Normative Agent Architectures , 2002, RASTA.

[39]  Guilin Qi,et al.  Approaches to Inconsistency Handling in Description-Logic Based Ontologies , 2007, OTM Workshops.

[40]  Wolfram Burgard,et al.  Conceptual spatial representations for indoor mobile robots , 2008, Robotics Auton. Syst..

[41]  David Chapman,et al.  Planning for Conjunctive Goals , 1987, Artif. Intell..

[42]  L. Stein,et al.  OWL Web Ontology Language - Reference , 2004 .

[43]  Javier Civera,et al.  Towards semantic SLAM using a monocular camera , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  Barbara Caputo,et al.  Multi-modal Semantic Place Classification , 2010, Int. J. Robotics Res..

[45]  Wolfram Burgard,et al.  Supervised Learning of Places from Range Data using AdaBoost , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[46]  David Brosset,et al.  Modeling Spatial Knowledge from Verbal Descriptions , 2013, COSIT.

[47]  Milind Tambe,et al.  Intelligent agents II : agent theories, architectures, and languages : IJCAI '95 Workshop (ATAL) : Montréal, Canada, August 19-20, 1995 : proceedings , 1996 .

[48]  Janos Gertler,et al.  Fault detection and diagnosis in engineering systems , 1998 .

[49]  Diego Calvanese,et al.  The Description Logic Handbook , 2007 .

[50]  Cipriano Galindo,et al.  Multi-hierarchical semantic maps for mobile robotics , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[51]  Francesco Chinello,et al.  KUKA Control Toolbox , 2011, IEEE Robotics & Automation Magazine.

[52]  Nico Blodow,et al.  Towards 3D Point cloud based object maps for household environments , 2008, Robotics Auton. Syst..

[53]  Koen V. Hindriks,et al.  Satisfying Maintenance Goals , 2007, DALT.

[54]  James J. Little,et al.  Curious George: An attentive semantic robot , 2008, Robotics Auton. Syst..

[55]  Guido Boella,et al.  An Architecture for Normative Reactive Agents , 2002, PRIMA.

[56]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[57]  Michael Luck,et al.  Understanding Permissions through Graphical Norms , 2010, DALT.