Ontology-Assisted Generalisation of Robot Action Execution Knowledge

When an autonomous robot learns how to execute actions, it is of interest to know if and when the execution policy can be generalised to variations of the learning scenarios. This can inform the robot about the necessity of additional learning, as using incomplete or unsuitable policies can lead to execution failures. Generalisation is particularly relevant when a robot has to deal with a large variety of objects and in different contexts. In this paper, we propose and analyse a strategy for generalising parameterised execution models of manipulation actions over different objects based on an object ontology. In particular, a robot transfers a known execution model to objects of related classes according to the ontology, but only if there is no other evidence that the model may be unsuitable. This allows using ontological knowledge as prior information that is then refined by the robot’s own experiences. We verify our algorithm for two actions - grasping and stowing everyday objects - such that we show that the robot can deduce cases in which an existing policy can generalise to other objects and when additional execution knowledge has to be acquired.

[1]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Koen V. Hindriks,et al.  Effective transfer learning of affordances for household robots , 2014, 4th International Conference on Development and Learning and on Epigenetic Robotics.

[3]  Rudolph Triebel,et al.  Persistent Anytime Learning of Objects from Unseen Classes , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[4]  Daniel Leidner,et al.  Things are made for what they are: Solving manipulation tasks by using functional object classes , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[5]  Siddhartha S. Srinivasa,et al.  The YCB object and Model set: Towards common benchmarks for manipulation research , 2015, 2015 International Conference on Advanced Robotics (ICAR).

[6]  Danica Kragic,et al.  Learning task constraints for robot grasping using graphical models , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Bipin Indurkhya,et al.  Adapting Everyday Manipulation Skills to Varied Scenarios , 2018, 2019 International Conference on Robotics and Automation (ICRA).

[8]  Elena Cabrio,et al.  Towards Lifelong Object Learning by Integrating Situated Robot Perception and Semantic Web Mining , 2016, ECAI.

[9]  Michael Beetz,et al.  Know Rob 2.0 — A 2nd Generation Knowledge Processing Framework for Cognition-Enabled Robotic Agents , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[10]  Xinyu Liu,et al.  Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.

[11]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[12]  David Paulius,et al.  A Survey of Knowledge Representation in Service Robotics , 2018, Robotics Auton. Syst..

[13]  Oleg O. Sushkov,et al.  Active robot learning of object properties , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  B. Martin.  PARAMETER ESTIMATION , 2012, Statistical Methods for Biomedical Research.

[15]  Claudio Zito,et al.  Feature-Based Transfer Learning for Robotic Push Manipulation , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[17]  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.

[18]  Daniel Leidner,et al.  Probabilistic Effect Prediction through Semantic Augmentation and Physical Simulation , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Mason Bretan,et al.  Learning Object Models For Non-prehensile Manipulation , 2019, 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[20]  CAGE: Context-Aware Grasping Engine , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[21]  Jeannette Bohg,et al.  Learning to Scaffold the Development of Robotic Manipulation Skills , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Oliver Kroemer,et al.  Generalizing pouring actions between objects using warped parameters , 2014, 2014 IEEE-RAS International Conference on Humanoid Robots.

[23]  Fuminori Saito,et al.  Development of Human Support Robot as the research platform of a domestic mobile manipulator , 2019 .

[24]  Pierre-Yves Oudeyer,et al.  Object Learning Through Active Exploration , 2014, IEEE Transactions on Autonomous Mental Development.

[25]  Florentin Wörgötter,et al.  Bootstrapping the Semantics of Tools: Affordance Analysis of Real World Objects on a Per-part Basis , 2016, IEEE Transactions on Cognitive and Developmental Systems.

[26]  Gerhard Lakemeyer,et al.  Representation and Experience-Based Learning of Explainable Models for Robot Action Execution , 2020, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[27]  Gerhard K. Kraetzschmar,et al.  Declarative specification of task-based grasping with constraint validation , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  Joanna Isabelle Olszewska,et al.  A review and comparison of ontology-based approaches to robot autonomy , 2019, The Knowledge Engineering Review.

[29]  Joachim Hertzberg,et al.  Finding Ways to Get the Job Done: An Affordance-Based Approach , 2014, ICAPS.