Kinematic noise propagation and grasp quality evaluation

To determine a force-closure grasp, current grasp synthesis algorithms either assume a deterministic model to compute a desired finger placement without noise, or model the end-effector position as a free-floating rigid body whose noise in pose is independent of the kinematic chain formed by the robot arm. In this work we instead explore a probabilistic approach that explicitly models noise in joint-angles. By sampling additive noise that is applied to a pre-grasp configuration and studying the resulting probability of force-closure when the robot fingers are closed, we observe in experiments that joint-angle positions can have a remarkable effect on the probability of successfully restraining an object. We systematically study the grasp quality value as a random variable and investigate the convergence of sampling based estimators for the mean, covariance and moments up to third order of this quantity by means of Montecarlo Sampling. We study illustrative examples of the impact of initial joint-configurations on the likelihood of force closure on a seven degree of freedom simulated Kuka lightweight robot arm.

[1]  Nancy S. Pollard,et al.  Parallel methods for synthesizing whole-hand grasps from generalized prototypes , 1994 .

[2]  Bo Wahlberg,et al.  A method for grasp evaluation based on disturbance force rejection , 2006, IEEE Transactions on Robotics.

[3]  Mathieu Aubry,et al.  Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[4]  Richard M. Murray,et al.  A Mathematical Introduction to Robotic Manipulation , 1994 .

[5]  Leslie Pack Kaelbling,et al.  Interactive Bayesian identification of kinematic mechanisms , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[6]  Danica Kragic,et al.  Multi-armed bandit models for 2D grasp planning with uncertainty , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[7]  Dan S. Necsulescu,et al.  Stochastic error propagation in robot arms , 1993, Adv. Robotics.

[8]  Peter K. Allen,et al.  Graspit! A versatile simulator for robotic grasping , 2004, IEEE Robotics & Automation Magazine.

[9]  Stefano Carpin,et al.  A fast algorithm for grasp quality evaluation using the object wrench space , 2015, 2015 IEEE International Conference on Automation Science and Engineering (CASE).

[10]  Sergey Levine,et al.  Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection , 2016, Int. J. Robotics Res..

[11]  Vijay Kumar,et al.  Robotic grasping and contact: a review , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[12]  Wolfram Burgard,et al.  A Probabilistic Framework for Learning Kinematic Models of Articulated Objects , 2011, J. Artif. Intell. Res..

[13]  Danica Kragic,et al.  Data-Driven Grasp Synthesis—A Survey , 2013, IEEE Transactions on Robotics.

[14]  Morten Strandberg,et al.  A grasp evaluation procedure based on disturbance forces , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[15]  Stefano Carpin,et al.  Fast grasp quality evaluation with partial convex hull computation , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[16]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[17]  Máximo A. Roa,et al.  Grasp quality measures: review and performance , 2014, Autonomous Robots.

[18]  Peter K. Allen,et al.  Pose error robust grasping from contact wrench space metrics , 2012, 2012 IEEE International Conference on Robotics and Automation.

[19]  Surya P. N. Singh,et al.  V-REP: A versatile and scalable robot simulation framework , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Shuzi Yang,et al.  Kinematic-Parameter Identification for Serial-Robot Calibration Based on POE Formula , 2010, IEEE Transactions on Robotics.

[21]  John F. Canny,et al.  Planning optimal grasps , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.