Learning tactile characterizations of object- and pose-specific grasps

Our aim is to predict the stability of a grasp from the perceptions available to a robot before attempting to lift up and transport an object. The percepts we consider consist of the tactile imprints and the object-gripper configuration read before and until the robot's manipulator is fully closed around an object. Our robot is equipped with multiple tactile sensing arrays and it is able to track the pose of an object during the application of a grasp. We present a kernel-logistic-regression model of pose- and touch-conditional grasp success probability which we train on grasp data collected by letting the robot experience the effect on tactile and visual signals of grasps suggested by a teacher, and letting the robot verify which grasps can be used to rigidly control the object. We consider models defined on several subspaces of our input data - e.g., using tactile perceptions or pose information only. Our experiment demonstrates that joint tactile and pose-based perceptions carry valuable grasp-related information, as models trained on both hand poses and tactile parameters perform better than the models trained exclusively on one perceptual input.

[1]  Roland S. Johansson,et al.  Sensory Control of Dexterous Manipulation in Humans , 1996 .

[2]  Henrik I. Christensen,et al.  Automatic grasp planning using shape primitives , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[3]  J. Flanagan,et al.  Hand and brain : the neurophysiology and psychology of hand movements , 1996 .

[4]  Imin Kao,et al.  Grasping, manipulation, and control with tactile sensing , 1990, Proceedings., IEEE International Conference on Robotics and Automation.

[5]  Gerd Hirzinger,et al.  Grasping the dice by dicing the grasp , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[6]  Peter K. Allen,et al.  Grasp Planning via Decomposition Trees , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[7]  M. Goodale,et al.  The visual brain in action , 1995 .

[8]  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..

[9]  Danica Kragic,et al.  Learning and Evaluation of the Approach Vector for Automatic Grasp Generation and Planning , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[10]  Ada Kritikos,et al.  Visual and tactile integration in action comprehension and execution , 2008, Brain Research.

[11]  Masashi Sugiyama,et al.  Semi-supervised speaker identification under covariate shift , 2010, Signal Process..

[12]  M. Vincze,et al.  BLORT-The Blocks World Robotic Vision Toolbox , 2010 .

[13]  Dominiek Reynaerts,et al.  Featureless classification of tactile contacts in a gripper using neural networks , 1996 .

[14]  Danica Kragic,et al.  Real-time tracking meets online grasp planning , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[15]  Robert D. Howe,et al.  Tactile sensing and control of robotic manipulation , 1993, Adv. Robotics.

[16]  R. Fisher.  Dispersion on a sphere , 1953, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[17]  P Jenmalm,et al.  Visual and tactile information about object-curvature control fingertip forces and grasp kinematics in human dexterous manipulation. , 2000, Journal of neurophysiology.

[18]  Angel P. del Pobil,et al.  Vision-tactile-force integration and robot physical interaction , 2009, 2009 IEEE International Conference on Robotics and Automation.

[19]  Hong Zhang,et al.  Control of contact via tactile sensing , 2000, IEEE Trans. Robotics Autom..

[20]  Peter K. AllenDepartment,et al.  CLOSED-LOOP VISUAL GRASPING AND MANIPULATIONBillibon , 1996 .

[21]  John Kenneth Salisbury,et al.  Augmentation of grasp robustness using intrinsic tactile sensing , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[22]  A. Morales,et al.  An Experiment in the Use of Manipulation Primitives and Tactile Perception for Reactive Grasping , 2007 .

[23]  C. Hesse,et al.  Grasping remembered objects: Exponential decay of the visual memory , 2010, Vision Research.

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

[25]  Danica Kragic,et al.  Evaluation of feature representation and machine learning methods in grasp stability learning , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[26]  Ashutosh Saxena,et al.  Robotic Grasping of Novel Objects using Vision , 2008, Int. J. Robotics Res..

[27]  Oliver Kroemer,et al.  Learning probabilistic discriminative models of grasp affordances under limited supervision , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[28]  R. S. Johansson,et al.  Roles of glabrous skin receptors and sensorimotor memory in automatic control of precision grip when lifting rougher or more slippery objects , 2004, Experimental Brain Research.

[29]  P. Allen,et al.  Dexterous Grasping via Eigengrasps : A Low-dimensional Approach to a High-complexity Problem , 2007 .

[30]  Erik B. Sudderth.  Graphical models for visual object recognition and tracking , 2006 .

[31]  R S Johansson,et al.  Sensory input and control of grip. , 1998, Novartis Foundation symposium.

[32]  A. Atiya,et al.  Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2005, IEEE Transactions on Neural Networks.

[33]  Ying Li,et al.  Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning , 2007, IEEE Transactions on Visualization and Computer Graphics.

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

[35]  Tamim Asfour,et al.  A potential field approach to dexterous tactile exploration of unknown objects , 2008, Humanoids 2008 - 8th IEEE-RAS International Conference on Humanoid Robots.

[36]  Robert Sessions Woodworth,et al.  THE ACCURACY OF VOLUNTARY MOVEMENT , 1899 .

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

[38]  Oliver Kroemer,et al.  Learning Continuous Grasp Affordances by Sensorimotor Exploration , 2010, From Motor Learning to Interaction Learning in Robots.