Which Object Comes Next? Grounded Order Completion by a Humanoid Robot

Abstract This paper describes a framework that a robot can use to complete the ordering of a set of objects. Given two sets of objects, an ordered set and an unordered set, the robot’s task is to select one object from the unordered set that best completes the ordering in the ordered set. In our experiments, the robot interacted with each object using a set of exploratory behaviors, while recording feedback from two sensory modalities (audio and proprioception). For each behavior and modality combination, the robot used the feedback sequence to estimate the perceptual distance for every pair of objects. The estimated object distance features were subsequently used to solve ordering tasks. The framework was tested on object completion tasks in which the objects varied by weight, compliance, and height. The robot was able to solve all of these tasks with a high degree of accuracy.

[1]  Tie-Yan Liu,et al.  Future directions in learning to rank , 2010, Yahoo! Learning to Rank Challenge.

[2]  L. Natale,et al.  Learning haptic representation of objects , 2004 .

[3]  James M. Rehg,et al.  Learning Visual Object Categories for Robot Affordance Prediction , 2010, Int. J. Robotics Res..

[4]  Jake K. Aggarwal,et al.  Object tracking in an outdoor environment using fusion of features and cameras , 2006, Image Vis. Comput..

[5]  Shuxiang Guo,et al.  Development of a Vectored Water-Jet-Based Spherical Underwater Vehicle , 2011 .

[6]  Jivko Sinapov,et al.  The odd one out task: Toward an intelligence test for robots , 2010, 2010 IEEE 9th International Conference on Development and Learning.

[7]  F. Graham,et al.  Learning of relative and absolute size concepts in preschool children , 1964 .

[8]  James J. Little,et al.  Informed visual search: Combining attention and object recognition , 2008, 2008 IEEE International Conference on Robotics and Automation.

[9]  Tomoaki Nakamura,et al.  Multimodal object categorization by a robot , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  L. Witmer.  The Montessori Method , 1914, The Psychological clinic.

[11]  Stefano Nolfi,et al.  Active perception: a sensorimotor account of object categorization , 2002 .

[12]  N. Todorov.  Intelligent Testing with the WISC-III,Kaufman Alan. S.,NY:John Wiley & Sons (1994),401 pp., $79.95 hardcover , 1995, Behaviour Change.

[13]  Jivko Sinapov,et al.  Vibrotactile Recognition and Categorization of Surfaces by a Humanoid Robot , 2011, IEEE Transactions on Robotics.

[14]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[15]  S. Sugarman.  The cognitive basis of classification in very young children: an analysis of object-ordering trends. , 1981, Child development.

[16]  G. Schreiber,et al.  The Fast Research Interface for the KUKA Lightweight Robot , 2022 .

[17]  Marc Carreras,et al.  Girona 500 AUV: From Survey to Intervention , 2012, IEEE/ASME Transactions on Mechatronics.

[18]  S. Gelman,et al.  Children's use of context in interpreting "big" and "little". , 1994, Child development.

[19]  S. Gelman,et al.  Coordination of size standards by young children. , 1988, Child development.

[20]  Charles Kemp,et al.  The discovery of structural form , 2008, Proceedings of the National Academy of Sciences.

[21]  J. Sinapov,et al.  Detecting the functional similarities between tools using a hierarchical representation of outcomes , 2008, 2008 7th IEEE International Conference on Development and Learning.

[22]  N. Funk.  A Study of the Kalman Filter applied to Visual Tracking , 2003 .

[23]  Jivko Sinapov,et al.  Object category recognition by a humanoid robot using behavior-grounded relational learning , 2011, 2011 IEEE International Conference on Robotics and Automation.

[24]  Dong-Soo Kwon,et al.  Control of underwater manipulators mounted on an ROV using base force information , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[25]  Minoru Asada,et al.  Object Category Acquisition by Dynamic Touch , 2008, Adv. Robotics.

[26]  P. H. Arx,et al.  Assessing Intellectual Giftedness with the WISC-IV and the IDS , 2008 .

[27]  A. Lillard.  Montessori: The Science Behind the Genius , 2005 .

[28]  Connor Schenck,et al.  Interactive object recognition using proprioceptive and auditory feedback , 2011, Int. J. Robotics Res..