Gathering and Conceptualizing Plan-Based Robot Activity Experiences

Learning from experiences is an effective approach to enhance robot’s competence. This paper focuses on developing capabilities for a robot to obtain robot activity experiences and conceptualize the experiences as plan schemata, which are used as heuristics for the robot to make plans in similar situations. The plan-based robot activity experiences are obtained through human-robot interactions where a teaching action from a command-line user interface triggers recording of an experience. To represent human-robot interaction activities, ontologies for experiences and user instructions are integrated into a robot ontology. Recorded experiences are episodic descriptions of the robot’s activities including relevant perceptions of the environment, the goals pursued, successes, and failures. Since the amount of experience data is large, a graph simplification algorithm based on ego networks is investigated to filter out irrelevant information in an experience. Finally, an approach to robot activity conceptualization based on deductive generalization and abstraction is presented. The proposed system was demonstrated in a scenario where a PR2 robot is taught how to “serve a coffee” to a guest, in the EU project RACE.

[1]  Yifan Hu,et al.  Efficient, High-Quality Force-Directed Graph Drawing , 2006 .

[2]  Alexandra Kirsch.  Robot learning language - Integrating programming and learning for cognitive systems , 2009, Robotics Auton. Syst..

[3]  Jürgen Schmidhuber,et al.  Autonomous learning of abstractions using Curiosity-Driven Modular Incremental Slow Feature Analysis , 2012, 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[4]  Mark E. J. Newman,et al.  Ego-centered networks and the ripple effect , 2001, Soc. Networks.

[5]  Alexandra Kirsch,et al.  Integration of programming and learning in a control language for autonomous robots performing everyday activities , 2008 .

[6]  Danica Kragic,et al.  Learning Task Models from Multiple Human Demonstrations , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[7]  Gi Hyun Lim,et al.  Interactive teaching and experience extraction for learning about objects and robot activities , 2014, The 23rd IEEE International Symposium on Robot and Human Interactive Communication.

[8]  Luís Seabra Lopes,et al.  FAILURE RECOVERY PLANNING FOR ROBOTIZED ASSEMBLY BASED ON LEARNED SEMANTIC STRUCTURES , 2007 .

[9]  Il Hong Suh,et al.  Ontology-Based Unified Robot Knowledge for Service Robots in Indoor Environments , 2011, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[10]  Joachim Hertzberg,et al.  Generating and Executing Hierarchical Mobile Manipulation Plans , 2014, ISR 2014.

[11]  Danica Kragic,et al.  Robot Learning from Demonstration: A Task-level Planning Approach , 2008 .

[12]  Monica N. Nicolescu,et al.  Natural methods for robot task learning: instructive demonstrations, generalization and practice , 2003, AAMAS '03.

[13]  Armando J. Pinho,et al.  An Ontology-based Multi-level Robot Architecture for Learning from Experiences , 2013, AAAI Spring Symposium: Designing Intelligent Robots.

[14]  Rüdiger Dillmann,et al.  Incremental Learning of Tasks From User Demonstrations, Past Experiences, and Vocal Comments , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Benjamin Kuipers,et al.  Autonomous Learning of High-Level States and Actions in Continuous Environments , 2012, IEEE Transactions on Autonomous Mental Development.

[16]  Ole Madsen,et al.  Human-robot interface for instructing industrial tasks using kinesthetic teaching , 2013, IEEE ISR 2013.

[17]  Gerald DeJong,et al.  Explanation-Based Acquisition of Planning Operators , 2006, ICAPS.

[18]  E. Tulving.  Episodic Memory and Autonoesis: Uniquely Human? , 2005 .