Adaptive Autonomous Grasp Selection via Pairwise Ranking

Autonomous grasp selection for robot pick-and-place applications makes use of either empirical methods leveraging object databases, which generate grasps for specific objects at the initial cost of modeling effort, or analytical methods, which generalize to novel objects but fail on object subsets that require specific grasping strategies not captured by the algorithm. We introduce a grasp selection algorithm that ranks grasp candidates with a set of grasp metrics augmented with object features, creating an approach that adapts its strategies based on user-specified grasp preferences. We formulate grasp selection as a pairwise ranking problem, which significantly reduces data collection compared to traditional grasp ranking methods and generalizes to novel objects. Our approach outperforms a state-of-the-art grasp calculation baseline and a pointwise ranking formulation of the same problem.

[1]  Tamim Asfour,et al.  Integrated Grasp Planning and Visual Object Localization For a Humanoid Robot with Five-Fingered Hands , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Tie-Yan Liu,et al.  Learning to Rank for Information Retrieval , 2011 .

[3]  Sonia Chernova,et al.  A Comparison of Remote Robot Teleoperation Interfaces for General Object Manipulation , 2017, 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI.

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

[5]  Dieter Fox,et al.  A large-scale hierarchical multi-view RGB-D object dataset , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Markus Vincze,et al.  Learning grasps with topographic features , 2015, Int. J. Robotics Res..

[7]  Eyke Hüllermeier,et al.  Preference Learning: An Introduction , 2010, Preference Learning.

[8]  Robert Platt,et al.  Using Geometry to Detect Grasp Poses in 3D Point Clouds , 2015, ISRR.

[9]  Xinyu Liu,et al.  Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics , 2017, Robotics: Science and Systems.

[10]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[11]  Peter K. Allen,et al.  Semantic grasping: planning task-specific stable robotic grasps , 2014, Auton. Robots.

[12]  James J. Kuffner,et al.  OpenRAVE: A Planning Architecture for Autonomous Robotics , 2008 .

[13]  Matei T. Ciocarlie,et al.  Collaborative grasp planning with multiple object representations , 2011, 2011 IEEE International Conference on Robotics and Automation.

[14]  Matei T. Ciocarlie,et al.  The Columbia grasp database , 2009, 2009 IEEE International Conference on Robotics and Automation.

[15]  Abhinav Gupta,et al.  Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours , 2015, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Stefanie Tellex,et al.  Autonomously Acquiring Instance-Based Object Models from Experience , 2015, ISRR.

[17]  T. Salakoski,et al.  Learning to Rank with Pairwise Regularized Least-Squares , 2007 .

[18]  Hang Li,et al.  Ranking refinement and its application to information retrieval , 2008, WWW.

[19]  Sonia Chernova,et al.  Construction of a 3D object recognition and manipulation database from grasp demonstrations , 2016, Auton. Robots.

[20]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[21]  Julie A. Shah,et al.  Apprenticeship Scheduling: Learning to Schedule from Human Experts , 2016, IJCAI.

[22]  Ricardo da Silva Torres,et al.  Exploiting pairwise recommendation and clustering strategies for image re-ranking , 2012, Inf. Sci..

[23]  Stefano Caselli,et al.  Part-based robot grasp planning from human demonstration , 2011, 2011 IEEE International Conference on Robotics and Automation.

[24]  Rüdiger Dillmann,et al.  An automatic grasp planning system for service robots , 2009, 2009 International Conference on Advanced Robotics.

[25]  Eric Brill,et al.  Beyond PageRank: machine learning for static ranking , 2006, WWW '06.

[26]  Rüdiger Dillmann,et al.  The KIT object models database: An object model database for object recognition, localization and manipulation in service robotics , 2012, Int. J. Robotics Res..

[27]  Robert Platt,et al.  Localizing Handle-Like Grasp Affordances in 3D Point Clouds , 2014, ISER.

[28]  Daniel King,et al.  Fetch & Freight : Standard Platforms for Service Robot Applications , 2016 .

[29]  Ashutosh Saxena,et al.  Efficient grasping from RGBD images: Learning using a new rectangle representation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[30]  S. Sathiya Keerthi,et al.  Efficient algorithms for ranking with SVMs , 2010, Information Retrieval.

[31]  Thorsten Joachims,et al.  Optimizing search engines using clickthrough data , 2002, KDD.

[32]  Matei T. Ciocarlie,et al.  Contact-reactive grasping of objects with partial shape information , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.