Part-based robot grasp planning from human demonstration

In this work we introduce a novel approach for robot grasp planning. The proposed method combines the benefits of programming by human demonstration for teaching appropriate grasps with those of automatic 3D shape segmentation for object recognition and semantic modeling. The work is motivated by important studies on human manipulation suggesting that when an object is perceived for grasping it is first parsed in its constituent parts. Following these findings we present a manipulation planning system capable of grasping objects by their parts which learns new tasks from human demonstration. The central advantage over previous approaches is the use of a topological method for shape segmentation enabling both object retrieval and part-based grasp planning according to the affordances of an object. Manipulation tasks are demonstrated in a virtual reality environment using a data glove. After the learning phase, each task is planned and executed in a robot environment that is able to generalize to similar, but previously unknown, objects

[1]  Donald D. Hoffman,et al.  Parts of recognition , 1984, Cognition.

[2]  I. Biederman Recognition-by-components: a theory of human image understanding. , 1987, Psychological review.

[3]  E. Reed The Ecological Approach to Visual Perception , 1989 .

[4]  Thierry Siméon,et al.  A manipulation planner for pick and place operations under continuous grasps and placements , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[5]  Tony Tung,et al.  Augmented Reeb graphs for content-based retrieval of 3D mesh models , 2004, Proceedings Shape Modeling Applications, 2004..

[6]  Stefano Caselli,et al.  Leveraging on a virtual environment for robot programming by demonstration , 2004, Robotics Auton. Syst..

[7]  Masayuki Nakajima,et al.  Detection and evaluation of grasping positions for autonomous agents , 2005, 2005 International Conference on Cyberworlds (CW'05).

[8]  Rüdiger Dillmann,et al.  Towards Cognitive Robots: Building Hierarchical Task Representations of Manipulations from Human Demonstration , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[9]  Yutaka Hirano,et al.  Image-based object recognition and dexterous hand/arm motion planning using RRTs for grasping in cluttered scene , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Gerd Hirzinger,et al.  Bridging the Gap between Task Planning and Path Planning , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Tamim Asfour,et al.  Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Tomás Lozano-Pérez,et al.  Imitation Learning of Whole-Body Grasps , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Kimitoshi Yamazaki,et al.  A grasp planning for picking up an unknown object for a mobile manipulator , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[14]  Roderic A. Grupen,et al.  A model of shared grasp affordances from demonstration , 2007, 2007 7th IEEE-RAS International Conference on Humanoid Robots.

[15]  Helge J. Ritter,et al.  Platform portable anthropomorphic grasping with the bielefeld 20-DOF shadow and 9-DOF TUM hand , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[17]  Ying Li,et al.  Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning , 2007, IEEE Transactions on Visualization and Computer Graphics.

[18]  Danica Kragic,et al.  Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[19]  Peter K. Allen,et al.  Grasp Planning via Decomposition Trees , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[20]  Daniel Cohen-Or,et al.  Part Analogies in Sets of Objects , 2008, 3DOR@Eurographics.

[21]  Marc Toussaint,et al.  Task maps in humanoid robot manipulation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Daniela Giorgi,et al.  Reeb graphs for shape analysis and applications , 2008, Theor. Comput. Sci..

[23]  Danica Kragic,et al.  Minimum volume bounding box decomposition for shape approximation in robot grasping , 2008, 2008 IEEE International Conference on Robotics and Automation.

[24]  Siddhartha S. Srinivasa,et al.  Grasp synthesis in cluttered environments for dexterous hands , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[25]  M. Zaslavskiy,et al.  A Path Following Algorithm for the Graph Matching Problem , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Anis Sahbani,et al.  A hybrid approach for grasping 3D objects , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Matei T. Ciocarlie,et al.  The Columbia grasp database , 2009, 2009 IEEE International Conference on Robotics and Automation.

[28]  Dejan Pangercic,et al.  Real-time CAD model matching for mobile manipulation and grasping , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[29]  Manuel Lopes,et al.  Learning grasping affordances from local visual descriptors , 2009, 2009 IEEE 8th International Conference on Development and Learning.

[30]  Alberto Del Bimbo,et al.  3D Mesh decomposition using Reeb graphs , 2009, Image Vis. Comput..