Determining proper grasp configurations for handovers through observation of object movement patterns and inter-object interactions during usage

We present a method for enabling robots to determine appropriate grasp configurations for handovers - i.e., where to grasp, and how to orient an object when handing it over. In our method, a robot first builds a knowledge base by observing demonstrations of how certain objects are used and their proper handover grasp configurations. Objects in the knowledge base are then organized based on their movements and inter-object interaction features. The key point in this process is that similarity in affordances should be recognized. When subsequently asked to handover an object, the robot then computes an appropriate grasp configuration based on the object's recognized affordances. Experimental results show that our method was able to differentiate and group together objects according to their affordances. Furthermore, when given a new object, our method was able to generalize data in the knowledge base and determine an appropriate grasp configuration.

[1]  Rachid Alami,et al.  Exploratory Study of a Robot Approaching a Person in the Context of Handing Over an Object , 2007, AAAI Spring Symposium: Multidisciplinary Collaboration for Socially Assistive Robotics.

[2]  Alois Knoll,et al.  Human-robot interaction in handing-over tasks , 2008, RO-MAN 2008 - The 17th IEEE International Symposium on Robot and Human Interactive Communication.

[3]  Andrea H. Mason,et al.  Grip forces when passing an object to a partner , 2005, Experimental Brain Research.

[4]  Danica Kragic,et al.  Visual object-action recognition: Inferring object affordances from human demonstration , 2011, Comput. Vis. Image Underst..

[5]  Stefanos Nikolaidis,et al.  Optimization of Temporal Dynamics for Adaptive Human-Robot Interaction in Assembly Manufacturing , 2012, Robotics: Science and Systems.

[6]  Elizabeth A. Croft,et al.  Grip forces and load forces in handovers: Implications for designing human-robot handover controllers , 2012, 2012 7th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[7]  Elizabeth A. Croft,et al.  A human-inspired object handover controller , 2013, Int. J. Robotics Res..

[8]  Siddhartha S. Srinivasa,et al.  Learning the communication of intent prior to physical collaboration , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[9]  Tetsunari Inamura,et al.  Bayesian learning of tool affordances based on generalization of functional feature to estimate effects of unseen tools , 2013, Artificial Life and Robotics.

[10]  Kerstin Dautenhahn,et al.  Robotic etiquette: Results from user studies involving a fetch and carry task , 2007, 2007 2nd ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[11]  Stefano Caselli,et al.  Comfortable robot to human object hand-over , 2012, 2012 IEEE RO-MAN: The 21st IEEE International Symposium on Robot and Human Interactive Communication.

[12]  Charles C. Kemp,et al.  Human-Robot Interaction for Cooperative Manipulation: Handing Objects to One Another , 2007, RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication.

[13]  L M Schleifer,et al.  Work Posture, Workstation Design, and Musculoskeletal Discomfort in a VDT Data Entry Task , 1991, Human factors.

[14]  Manuel Lopes,et al.  Learning Object Affordances: From Sensory--Motor Coordination to Imitation , 2008, IEEE Transactions on Robotics.

[15]  Yangsheng Xu,et al.  Development of a hospital service robot for transporting task , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[16]  Tiffany L. Chen,et al.  Hand it over or set it down: A user study of object delivery with an assistive mobile manipulator , 2009, RO-MAN 2009 - The 18th IEEE International Symposium on Robot and Human Interactive Communication.

[17]  Siddhartha S. Srinivasa,et al.  Using spatial and temporal contrast for fluent robot-human hand-overs , 2011, 2011 6th ACM/IEEE International Conference on Human-Robot Interaction (HRI).

[18]  Hema Swetha Koppula,et al.  Learning human activities and object affordances from RGB-D videos , 2012, Int. J. Robotics Res..

[19]  Siddhartha S. Srinivasa,et al.  Human preferences for robot-human hand-over configurations , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Thomas Feix,et al.  A comprehensive grasp taxonomy , 2009 .

[21]  Anthony G. Cohn,et al.  Learning Functional Object-Categories from a Relational Spatio-Temporal Representation , 2008, ECAI.