Learning robot tactile sensing for object manipulation

Tactile sensing is a fundamental component of object manipulation and tool handling skills. With robots entering unstructured environments, tactile feedback also becomes an important ability for robot manipulation. In this work, we explore how a robot can learn to use tactile sensing in object manipulation tasks. We first address the problem of in-hand object localization and adapt three pose estimation algorithms from computer vision. Second, we employ dynamic motor primitives to learn robot movements from human demonstrations and record desired tactile signal trajectories. Then, we add tactile feedback to the control loop and apply relative entropy policy search to learn the parameters of the tactile coupling. Additionally, we show how the learning of tactile feedback can be performed more efficiently by reducing the dimensionality of the tactile information through spectral clustering and principal component analysis. Our approach is implemented on a real robot, which learns to perform a scraping task with a spatula in an altered environment.

[1]  E. Forgy,et al.  Cluster analysis of multivariate data : efficiency versus interpretability of classifications , 1965 .

[2]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[3]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[4]  Joseph B. Kruskall,et al.  The Symmetric Time-Warping Problem : From Continuous to Discrete , 1983 .

[5]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[6]  Gérard G. Medioni,et al.  Object modeling by registration of multiple range images , 1991, Proceedings. 1991 IEEE International Conference on Robotics and Automation.

[7]  Gérard G. Medioni,et al.  Object modelling by registration of multiple range images , 1992, Image Vis. Comput..

[8]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[9]  Yizong Cheng,et al.  Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Jitendra Malik,et al.  Normalized cuts and image segmentation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[11]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[12]  Claudio Melchiorri,et al.  Slip detection and control using tactile and force sensors , 2000 .

[13]  Jun Nakanishi,et al.  Movement imitation with nonlinear dynamical systems in humanoid robots , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[14]  William T. Freeman,et al.  Nonparametric belief propagation , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[15]  William T. Freeman,et al.  Efficient Multiscale Sampling from Products of Gaussian Mixtures , 2003, NIPS.

[16]  Takayuki Koizumi,et al.  Slip detection with distributed-type tactile sensor , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[17]  Eamonn J. Keogh,et al.  On the Need for Time Series Data Mining Benchmarks: A Survey and Empirical Demonstration , 2002, Data Mining and Knowledge Discovery.

[18]  Zhengyou Zhang,et al.  Iterative point matching for registration of free-form curves and surfaces , 1994, International Journal of Computer Vision.

[19]  Antonio Torralba,et al.  Depth from Familiar Objects: A Hierarchical Model for 3D Scenes , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[20]  Oussama Khatib,et al.  Bayesian estimation for autonomous object manipulation based on tactile sensors , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[21]  Stefan Schaal,et al.  Policy Gradient Methods for Robotics , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

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

[23]  Ulrike von Luxburg,et al.  A tutorial on spectral clustering , 2007, Stat. Comput..

[24]  Betty J. Mohler,et al.  Learning perceptual coupling for motor primitives , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[25]  J. Randall Flanagan,et al.  Coding and use of tactile signals from the fingertips in object manipulation tasks , 2009, Nature Reviews Neuroscience.

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

[27]  Justus H. Piater,et al.  A Probabilistic Framework for 3D Visual Object Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[28]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[29]  Silvio Savarese,et al.  A multi-view probabilistic model for 3D object classes , 2009, CVPR.

[30]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Oliver Kroemer,et al.  Combining active learning and reactive control for robot grasping , 2010, Robotics Auton. Syst..

[32]  Yasemin Altun,et al.  Relative Entropy Policy Search , 2010 .

[33]  Dieter Fox,et al.  Kernel Descriptors for Visual Recognition , 2010, NIPS.

[34]  Leslie Pack Kaelbling,et al.  Task-Driven Tactile Exploration , 2010, Robotics: Science and Systems.

[35]  Craig Corcoran,et al.  A measurement model for tracking hand-object state during dexterous manipulation , 2010, 2010 IEEE International Conference on Robotics and Automation.

[36]  Jan Peters,et al.  Learning table tennis with a Mixture of Motor Primitives , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[37]  Christoph H. Lampert,et al.  Learning Dynamic Tactile Sensing With Robust Vision-Based Training , 2011, IEEE Transactions on Robotics.

[38]  Roozbeh Mottaghi,et al.  A compositional approach to learning part-based models of objects , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[39]  Ronen Basri,et al.  Viewpoint-aware object detection and pose estimation , 2011, 2011 International Conference on Computer Vision.

[40]  Wolfram Burgard,et al.  Tactile Sensing for Mobile Manipulation , 2011, IEEE Transactions on Robotics.

[41]  Jimmy A. Jørgensen,et al.  Assessing Grasp Stability Based on Learning and Haptic Data , 2011, IEEE Transactions on Robotics.

[42]  Oliver Kroemer,et al.  Learning to select and generalize striking movements in robot table tennis , 2012, AAAI Fall Symposium: Robots Learning Interactively from Human Teachers.

[43]  Rongqiang Liu,et al.  Slip detection by array-type pressure sensor for a grasp task , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[44]  Stefan Schaal,et al.  Towards Associative Skill Memories , 2012, 2012 12th IEEE-RAS International Conference on Humanoid Robots (Humanoids 2012).

[45]  Jan Peters,et al.  A Survey on Policy Search for Robotics , 2013, Found. Trends Robotics.

[46]  Peter Englert,et al.  Probabilistic model-based imitation learning , 2013, Adapt. Behav..

[47]  Edward H. Adelson,et al.  Localization and manipulation of small parts using GelSight tactile sensing , 2014, IROS.

[48]  Peter K. Allen,et al.  Stable grasping under pose uncertainty using tactile feedback , 2014, Auton. Robots.

[49]  Oliver Kroemer,et al.  Learning to predict phases of manipulation tasks as hidden states , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).