Manipulation planning of similar objects by part correspondence

Many innovative ideas in robotics have been inspired by neuroscience and, in particular, by the investigation of how intelligence and perception work. In this paper we explore an approach for semantic robot grasping, which combines programming by demonstration, automatic 3D shape segmentation and manipulation planning by parts. Neuro-psychology studies have evidenced the influence of shape decomposition for human perception of objects. In accordance to these findings a robot manipulation system is presented which is capable of learning and planning manipulation tasks for similar objects. The proposed approach allows a robot to perform intelligent grasping tasks by modeling the topology of an object. Manipulation tasks are demonstrated in virtual reality using a data glove. Results show that 3D shape segmentation enables both object retrieval and part-based grasping according to the affordances of an object.

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

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

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

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

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

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

[7]  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.

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

[9]  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.

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

[11]  Stefano Caselli,et al.  Part-based robot grasp planning from human demonstration , 2011, 2011 IEEE International Conference on Robotics and Automation.

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

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

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

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

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

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