Haptic classification and recognition of objects using a tactile sensing forearm

In this paper, we demonstrate data-driven inference of mechanical properties of objects using a tactile sensor array (skin) covering a robot's forearm. We focus on the mobility (sliding vs. fixed), compliance (soft vs. hard), and identity of objects in the environment, as this information could be useful for efficient manipulation and search. By using the large surface area of the forearm, a robot could potentially search and map a cluttered volume more efficiently, and be informed by incidental contact during other manipulation tasks. Our approach tracks a contact region on the forearm over time in order to generate time series of select features, such as the maximum force, contact area, and contact motion. We then process and reduce the dimensionality of these time series to generate a feature vector to characterize the contact. Finally, we use the k-nearest neighbor algorithm (k-NN) to classify a new feature vector based on a set of previously collected feature vectors. Our results show a high cross-validation accuracy in both classification of mechanical properties and object recognition. In addition, we analyze the effect of taxel resolution, duration of observation, feature selection, and feature scaling on the classification accuracy.

[1]  Gunter Dueck,et al.  Highly Sensitive! , 2005, Informatik-Spektrum.

[2]  Gert Kootstra,et al.  Classification of rigid and deformable objects using a novel tactile sensor , 2011, 2011 15th International Conference on Advanced Robotics (ICAR).

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

[4]  L.A. Jones,et al.  Material identification using real and simulated thermal cues , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Heinz Wörn,et al.  Haptic object recognition using passive joints and haptic key features , 2010, 2010 IEEE International Conference on Robotics and Automation.

[6]  Wolfram Burgard,et al.  Learning the elasticity parameters of deformable objects with a manipulation robot , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Oliver Brock,et al.  Manipulating articulated objects with interactive perception , 2008, 2008 IEEE International Conference on Robotics and Automation.

[8]  Wolfram Burgard,et al.  Learning Deformable Object Models for Mobile Robot Navigation using Depth Cameras and a Manipulation Robot , 2010 .

[9]  A. Ng,et al.  Touch Based Perception for Object Manipulation , 2007 .

[10]  Aaron M. Dollar,et al.  Benchmarking grasping and manipulation: Properties of the Objects of Daily Living , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[11]  Francesco Zanichelli,et al.  Haptic object recognition with a dextrous hand based on volumetric shape representations , 1994, Proceedings of 1994 IEEE International Conference on MFI '94. Multisensor Fusion and Integration for Intelligent Systems.

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

[13]  Koh Hosoda,et al.  Robust haptic recognition by anthropomorphic bionic hand through dynamic interaction , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Jivko Sinapov,et al.  Interactive learning of the acoustic properties of household objects , 2009, 2009 IEEE International Conference on Robotics and Automation.

[15]  Thenkurussi Kesavadas,et al.  Material Property Recognition by Active Tapping for Fingertip Digitizing , 2006, 2006 14th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems.

[16]  Robert W. Platt,et al.  Using Bayesian filtering to interpret tactile data during flexible materials manipulation , 2010 .

[17]  Eugenio Faldella,et al.  A neural approach to robotic haptic recognition of 3-D objects based on a Kohonen self-organizing feature map , 1997, IEEE Trans. Ind. Electron..

[18]  C. Schenck,et al.  Interactive Object Recognition Using Proprioceptive Feedback , 2009 .

[19]  S.V. Dudul,et al.  Sensor for Classification of Material Type and Its Surface Properties Using Radial Basis Networks , 2008, IEEE Sensors Journal.

[20]  Jivko Sinapov,et al.  Vibrotactile Recognition of Surface Textures by a Humanoid Robot , 2009 .

[21]  Wolfram Burgard,et al.  Object identification with tactile sensors using bag-of-features , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Ryo Kikuuwe,et al.  Recognizing object surface properties using impedance perception , 2003, MHS2003. Proceedings of 2003 International Symposium on Micromechatronics and Human Science (IEEE Cat. No.03TH8717).

[23]  Hiromi T. Tanaka,et al.  Extracting rheological properties of deformable objects with Haptic vision , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[24]  Jivko Sinapov,et al.  A Behavior-Grounded Approach to Forming Object Categories: Separating Containers From Noncontainers , 2012, IEEE Transactions on Autonomous Mental Development.

[25]  Gregory D. Hager,et al.  Tactile-Object Recognition From Appearance Information , 2011, IEEE Transactions on Robotics.

[26]  Kenneth S. Roberts,et al.  Haptic object recognition using a multi-fingered dextrous hand , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[27]  Isabelle Guyon,et al.  An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..

[28]  S. Takamuku,et al.  Haptic discrimination of material properties by a robotic hand , 2007, 2007 IEEE 6th International Conference on Development and Learning.