Learning generalizable surface cleaning actions from demonstration

When surveyed, potential users often report cleaning as a desired robot capability. Cleaning tasks, such as dusting, wiping, or scrubbing, involve applying a tool on a surface. A general-purpose robotic solution to household cleaning needs to address manipulation of the numerous cleaning tools made for different purposes. Finding a universal solution to this manipulation problem is extremely challenging and it is not feasible for developers to pre-program the robot to use every possible tool. Instead, our work seeks to allow end users to program robots by demonstration using their own specific tools. We propose a method to extract a compact representation of a cleaning action from a single demonstration, such that the tool can be applied on different surfaces. The method exploits key insights about tool directionality and constraints placed on the provided demonstration. We demonstrate that our method is able to reliably learn cleaning actions for six different tools and apply those actions on different testing surfaces, even ones smaller than the training surface. Our method reproduces the cleaning performance of the demonstrated trajectory when applied on the training surface and it captures different user preferences.

[1]  Maya Cakmak,et al.  Keyframe-based Learning from Demonstration , 2012, Int. J. Soc. Robotics.

[2]  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).

[3]  Guido Bugmann,et al.  What Can a Personal Robot Do for You? , 2011, TAROS.

[4]  Stefan Schaal,et al.  Robot Programming by Demonstration , 2009, Springer Handbook of Robotics.

[5]  Maya Cakmak,et al.  Enhanced robotic cleaning with a low-cost tool attachment , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  C. Kemp,et al.  Robot Manipulation of Human Tools : Autonomous Detection and Control of Task Relevant Features , 2006 .

[7]  Wolfram Burgard,et al.  Imitation learning with generalized task descriptions , 2009, 2009 IEEE International Conference on Robotics and Automation.

[8]  Jun Nakanishi,et al.  Learning Movement Primitives , 2005, ISRR.

[9]  Marc Carreras,et al.  A survey on coverage path planning for robotics , 2013, Robotics Auton. Syst..

[10]  W. Burgard,et al.  Learning the State Transition Model to Efficiently Clean Surfaces with Mobile Manipulation Robots , 2011 .

[11]  Takeo Igarashi,et al.  Sketch and run: a stroke-based interface for home robots , 2009, CHI.

[12]  Wendy A. Rogers,et al.  Understanding the Potential for Robot Assistance for Older Adults in the Home Environment , 2011 .

[13]  Andrea Lockerd Thomaz,et al.  Teachable robots: Understanding human teaching behavior to build more effective robot learners , 2008, Artif. Intell..

[14]  Paolo Fiorini,et al.  A Short History of Cleaning Robots , 2000, Auton. Robots.

[15]  Maya Cakmak,et al.  Designing robot learners that ask good questions , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[16]  Alexander Stoytchev,et al.  Behavior-Grounded Representation of Tool Affordances , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[17]  Leila Takayama,et al.  Exploring the role of robots in home organization , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[18]  Pieter Abbeel,et al.  Learning from Demonstrations Through the Use of Non-rigid Registration , 2013, ISRR.

[19]  Maya Cakmak,et al.  Robot Programming by Demonstration with Interactive Action Visualizations , 2014, Robotics: Science and Systems.

[20]  Aude Billard,et al.  Statistical Learning by Imitation of Competing Constraints in Joint Space and Task Space , 2009, Adv. Robotics.

[21]  Alin Albu-Schäffer,et al.  Learning from demonstration: repetitive movements for autonomous service robotics , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[22]  Charles C. Kemp,et al.  Manipulation in Human Environments , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[23]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[24]  Manfred Tscheligi,et al.  Teaching a humanoid: A user study on learning by demonstration with HOAP-3 , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.