Autonomous Robots: Special Issue on Autonomous Mobile Manipulation Manuscript No. Generality and Legibility in Mobile Manipulation Learning Skills for Routine Tasks

This article investigates methods for achieving more general manipulation capabilities for mobile manipulation platforms, which produce legible behavior in human living environments. To achieve generality and legibility, we combine two control mechanisms. First of all, experienceand observation-based learning of skills is applied to routine tasks, so that the repetitive and stereotypical character of everyday activity is exploited. Second, we use planning, reasoning, and search for novel tasks which have no stereotypical solution. We apply these ideas to the learning and use of action-related places, to the model-based visual recognition and localization of objects, and the learning and application of reaching strategies and motions from humans. We demonstrate the integration of these mechanisms into a single low-level control system for autonomous manipulation platforms.

[1]  Masayuki Inaba,et al.  Vision based behavior verification system of humanoid robot for daily environment tasks , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[2]  Daniel M. Wolpert,et al.  Making smooth moves , 2022 .

[3]  Richard T. Vaughan,et al.  The Player/Stage Project: Tools for Multi-Robot and Distributed Sensor Systems , 2003 .

[4]  Tamar Flash,et al.  Motor primitives in vertebrates and invertebrates , 2005, Current Opinion in Neurobiology.

[5]  E. Todorov Optimality principles in sensorimotor control , 2004, Nature Neuroscience.

[6]  Gerd Hirzinger,et al.  Capturing robot workspace structure: representing robot capabilities , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Zoubin Ghahramani,et al.  Computational principles of movement neuroscience , 2000, Nature Neuroscience.

[8]  Michael Beetz,et al.  Positioning mobile manipulators to perform constrained linear trajectories , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Odest Chadwicke Jenkins,et al.  Manipulation Manifolds : Explorations into Uncovering Manifolds in Sensory-motor Spaces , 2006 .

[10]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[11]  Steven M. LaValle,et al.  Planning algorithms , 2006 .

[12]  Gordon Cheng,et al.  Human-humanoid interaction: is a humanoid robot perceived as a human? , 2004, 4th IEEE/RAS International Conference on Humanoid Robots, 2004..

[13]  G. Niemeyer,et al.  Springer Handbook of Robotics: Chapter 31 , 2008 .

[14]  Michael Beetz,et al.  Seamless Execution of Action Sequences , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[15]  Aude Billard,et al.  On Learning, Representing, and Generalizing a Task in a Humanoid Robot , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[16]  Ian Horswill Integrated systems and naturalistic tasks , 1996, CSUR.

[17]  Tamim Asfour,et al.  ARMAR-III: An Integrated Humanoid Platform for Sensory-Motor Control , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[18]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[19]  R. Cohen,et al.  Plans for grasping objects , 2006 .

[20]  Alcherio Martinoli,et al.  A quantitative method for comparing trajectories of mobile robots using point distribution models , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[21]  Giorgio Metta,et al.  YARP: Yet Another Robot Platform , 2006 .

[22]  Alin Albu-Schäffer,et al.  The DLR lightweight robot: design and control concepts for robots in human environments , 2007, Ind. Robot.

[23]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[24]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[25]  Anthony A. Maciejewski,et al.  The Singular Value Decomposition: Computation and Applications to Robotics , 1989, Int. J. Robotics Res..

[26]  Karen Zita Haigh,et al.  Situation-dependent learning for interleaved planning and robot execution , 1998 .

[27]  Aude Billard,et al.  Handbook of Robotics Chapter 59 : Robot Programming by Demonstration , 2007 .

[28]  Michael Beetz,et al.  Compact models of human reaching motions for robotic control in everyday manipulation tasks , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[29]  Stefan Schaal,et al.  Movement reproduction and obstacle avoidance with dynamic movement primitives and potential fields , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[30]  Danica Kragic,et al.  Vision for robotic object manipulation in domestic settings , 2005, Robotics Auton. Syst..

[31]  RusuRadu Bogdan,et al.  Towards 3D Point cloud based object maps for household environments , 2008 .

[32]  Drew McDermott,et al.  A reactive plan language , 1991 .

[33]  Nico Blodow,et al.  The Assistive Kitchen — A demonstration scenario for cognitive technical systems , 2007, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[34]  Bernd Radig,et al.  Learning Local Objective Functions for Robust Face Model Fitting , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Oliver Brock,et al.  The UMass Mobile Manipulator UMan: An Experimental Platform for Autonomous Mobile Manipulation , 2006 .

[36]  Siddhartha S. Srinivasa,et al.  Manipulation planning with Workspace Goal Regions , 2009, 2009 IEEE International Conference on Robotics and Automation.

[37]  Michael Beetz,et al.  Refining the Execution of Abstract Actions with Learned Action Models , 2008, J. Artif. Intell. Res..

[38]  Andrew Y. Ng,et al.  Probabilistic Mobile Manipulation in Dynamic Environments, with Application to Opening Doors , 2007, IJCAI.

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

[40]  Michael Beetz,et al.  Transformational planning for mobile manipulation based on action-related places , 2009, 2009 International Conference on Advanced Robotics.

[41]  Derek Long,et al.  Using learned action models in execution monitoring , 2006 .

[42]  E. Torres-Jara,et al.  Challenges for Robot Manipulation in Human Environments , 2006 .

[43]  Siddhartha S. Srinivasa,et al.  The robotic busboy: Steps towards developing a mobile robotic home assistant , 2008 .

[44]  Michael Beetz,et al.  3D model selection from an internet database for robotic vision , 2009, 2009 IEEE International Conference on Robotics and Automation.

[45]  Jun Morimoto,et al.  CB: A Humanoid Research Platform for Exploring NeuroScience , 2006, 2006 6th IEEE-RAS International Conference on Humanoid Robots.

[46]  Yiannis Demiris,et al.  Learning Forward Models for Robots , 2005, IJCAI.

[47]  Vincent Lepetit,et al.  Keypoint recognition using randomized trees , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[48]  Universitäts und Landesbibliothek Bonn Plan Projection, Execution, and Learning for Mobile Robot Control , 2004 .

[49]  Nassir Navab,et al.  Harmonic Deformation Model for Edge Based Template Matching , 2008, VISAPP.

[50]  Moritz Tenorth,et al.  Understanding and executing instructions for everyday manipulation tasks from the World Wide Web , 2010, 2010 IEEE International Conference on Robotics and Automation.

[51]  Markus Ulrich,et al.  Recognition and Tracking of 3D Objects , 2008, DAGM-Symposium.

[52]  Hong Liu,et al.  DLR hand II: experiments and experience with an anthropomorphic hand , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[53]  Bernard Chazelle,et al.  Shape distributions , 2002, TOGS.

[54]  Dmitry Berenson,et al.  Grasp planning in complex scenes , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[55]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[56]  Stefan Schaal,et al.  Biologically-inspired dynamical systems for movement generation: Automatic real-time goal adaptation and obstacle avoidance , 2009, 2009 IEEE International Conference on Robotics and Automation.