Active Multi-Contact Continuous Tactile Exploration with Gaussian Process Differential Entropy

In the present work, we propose an active tactile exploration framework to obtain a surface model of an unknown object utilizing multiple contacts simultaneously. To incorporate these multiple contacts, the exploration strategy is based on the differential entropy of the underlying Gaussian process implicit surface model, which formalizes the exploration with multiple contacts within an information theoretic context and additionally allows for nonmyopic multi-step planning. In contrast to many previous approaches, the robot continuously (a slides along the surface with its end-effectors to gather the tactile stimuli, instead of touching it at discrete locations. This is realized by closely integrating the surface model into the compliant controller framework. Furthermore, we extend our recently proposed sliding based tactile exploration approach to handle non-convex objects. In the experiments, it is shown that multiple contacts simultaneously leads to a more efficient exploration of complex, non-convex objects, not only in terms of time, but also with respect to the total moved distance of all end-effectors. Finally, we demonstrate our methodology with a real PR2 robot that explores an object with both of its arms.

[1]  Philipp Hennig,et al.  Entropy Search for Information-Efficient Global Optimization , 2011, J. Mach. Learn. Res..

[2]  Jan Peters,et al.  Active tactile object exploration with Gaussian processes , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[3]  Peter Englert,et al.  Constrained Bayesian optimization of combined interaction force/task space controllers for manipulations , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Aude Billard,et al.  Multi-contact haptic exploration and grasping with tactile sensors , 2016, Robotics Auton. Syst..

[5]  Claudio Zito,et al.  GPAtlasRRT: A Local Tactile Exploration Planner for Recovering the Shape of Novel Objects , 2018, Int. J. Humanoid Robotics.

[6]  Peter Englert,et al.  Active learning with query paths for tactile object shape exploration , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[7]  Iain Murray,et al.  Differentiation of the Cholesky decomposition , 2016, ArXiv.

[8]  Helge J. Ritter,et al.  A Control Framework for Tactile Servoing , 2013, Robotics: Science and Systems.

[9]  Danica Kragic,et al.  Enhancing visual perception of shape through tactile glances , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Geoffrey A. Hollinger,et al.  Active planning for underwater inspection and the benefit of adaptivity , 2012, Int. J. Robotics Res..

[11]  Marc Toussaint,et al.  Gaussian process implicit surfaces for shape estimation and grasping , 2011, 2011 IEEE International Conference on Robotics and Automation.

[12]  Tomonori Yamamoto,et al.  Use of tactile feedback to control exploratory movements to characterize object compliance , 2012, Front. Neurorobot..

[13]  Andreas Krause,et al.  Near-optimal Nonmyopic Value of Information in Graphical Models , 2005, UAI.

[14]  R. Klatzky,et al.  Haptic perception: A tutorial , 2009, Attention, perception & psychophysics.

[15]  Natale Lorenzo,et al.  Active perception: Building objects' models using tactile exploration , 2016 .

[16]  Marc Toussaint,et al.  Uncertainty aware grasping and tactile exploration , 2013, 2013 IEEE International Conference on Robotics and Automation.

[17]  Edward H. Adelson,et al.  3D Shape Perception from Monocular Vision, Touch, and Shape Priors , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[18]  A. Bernardino,et al.  Active Tactile Exploration for Grasping , 2013 .

[19]  Gordon Cheng,et al.  Tactile-based active object discrimination and target object search in an unknown workspace , 2018, Autonomous Robots.

[20]  Nathan F. Lepora,et al.  Exploratory Tactile Servoing With Active Touch , 2017, IEEE Robotics and Automation Letters.

[21]  Fabio Tozeto Ramos,et al.  Bayesian Optimisation for informative continuous path planning , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Andrew Fitzgibbon,et al.  Gaussian Process Implicit Surfaces , 2006 .

[23]  Alexander Bierbaum,et al.  Grasp affordances from multi-fingered tactile exploration using dynamic potential fields , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[24]  Tamim Asfour,et al.  Active Tactile Exploration Based on Cost-Aware Information Gain Maximization , 2018, Int. J. Humanoid Robotics.

[25]  Duy Nguyen-Tuong,et al.  Safe Exploration for Active Learning with Gaussian Processes , 2015, ECML/PKDD.