Direct Perception and Action Decision for Unknown Object Grasping

This paper discusses the direct perception of an unknown object and the action decision to grasp an unknown object using depth sensor for social robots. Conventional methods estimate the accurate physical parameters when a robot wants to grasp an unknown object. Therefore, we propose a perceptual system based on an invariant concept in ecological psychology, which perceives the information relevant to the action of the robot. Firstly, we proposed the plane detection based approach for perceiving an unknown object. In this paper, we propose the sensation of grasping which is expressed by using inertia tensor, and applied with fuzzy inference using the relation between principle moment of inertia. The sensation of grasping encourages the decision for the grasping action directly without inferring from physical value such as size, posture and shape. As experimental results, we show that the sensation of grasping expresses the relative position and posture between the robot and the object, and the embodiment of the robot arm by one parameter. And, we verify the validity of the action decision from the sensation of grasping. Direct Perception and Action Decision for Unknown Object Grasping

[1]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Vaishali Kulkarni,et al.  BEASF-Based Image Enhancement for Caries Detection Using Multidimensional Projection and Neural Network , 2018, Int. J. Artif. Life Res..

[3]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[4]  Naoyuki Kubota,et al.  Control Architecture for Human Friendly Robots Based on Interacting with Human , 2011, ICIRA.

[5]  Aude Billard,et al.  Robotic assistants in therapy and education of children with autism: can a small humanoid robot help encourage social interaction skills? , 2005, Universal Access in the Information Society.

[6]  Jiri Matas,et al.  Matching with PROSAC - progressive sample consensus , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  Dorin Comaniciu,et al.  Real-time tracking of non-rigid objects using mean shift , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[8]  Carme Torras,et al.  ToF cameras for active vision in robotics , 2014 .

[9]  Zoltan-Csaba Marton,et al.  Tutorial: Point Cloud Library: Three-Dimensional Object Recognition and 6 DOF Pose Estimation , 2012, IEEE Robotics & Automation Magazine.

[10]  Hun-ok Lim,et al.  Unknown object extraction for robot partner using depth sensor , 2015, 2015 International Conference on Informatics, Electronics & Vision (ICIEV).

[11]  Beatrice Alenljung,et al.  User Experience of Socially Interactive Robots : Its Role and Relevance , 2015 .

[12]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[13]  George J. Pappas,et al.  Nonmyopic View Planning for Active Object Classification and Pose Estimation , 2014, IEEE Transactions on Robotics.

[14]  Michel Dhome,et al.  Real time 3D template matching , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[15]  Hun-ok Lim,et al.  3D plane detection for robot perception applying particle swarm optimization , 2014, 2014 World Automation Congress (WAC).

[16]  J. K. Mandal,et al.  Handbook of Research on Natural Computing for Optimization Problems , 2016 .

[17]  Gonzalo E. Reyes,et al.  The Logical Foundations of Cognition , 1994 .

[18]  Dejan Pangercic,et al.  Robotic roommates making pancakes , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[19]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[20]  Fumio Harashima,et al.  Natural Interface Using Pointing Behavior for Human–Robot Gestural Interaction , 2007, IEEE Transactions on Industrial Electronics.

[21]  Hiroshi Ishiguro,et al.  What makes people accept a robot in a social environment - discussion from six-week study in an office - , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[23]  Andreas Zell,et al.  Real-time plane extraction from depth images with the Randomized Hough Transform , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[24]  M. Turvey,et al.  Weight perception and the haptic size-weight illusion are functions of the inertia tensor. , 1996, Journal of experimental psychology. Human perception and performance.

[25]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[26]  Marco Ceccarelli,et al.  Service Robots and Robotics: Design and Application , 2012 .

[27]  João Carlos Mendes Carvalho,et al.  Robot Modeling for Physical Rehabilitation , 2012 .

[28]  Michael Lehmann,et al.  An all-solid-state optical range camera for 3D real-time imaging with sub-centimeter depth resolution (SwissRanger) , 2004, SPIE Optical Systems Design.

[29]  Illah R. Nourbakhsh,et al.  A survey of socially interactive robots , 2003, Robotics Auton. Syst..

[30]  Maja J. Mataric,et al.  The role of physical embodiment in human-robot interaction , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[31]  Ebrahim Oshni Alvandi.  Emotions and Information Processing: A Theoretical Approach , 2011, Int. J. Synth. Emot..