Learning contact locations for pushing and orienting unknown objects

We present a method by which a robot learns to predict effective contact locations for pushing as a function of object shape. The robot performs push experiments at many contact locations on multiple objects and records local and global shape features at each point of contact. Each trial attempts to either push the object in a straight line or to rotate the object to a new orientation. The robot observes the outcome trajectories of the manipulations and computes either a push-stability or rotate-push score for each trial. The robot then learns a regression function for each score in order to predict push effectiveness as a function of object shape. With this mapping, the robot can infer effective push locations for subsequent objects from their shapes, regardless of whether they belong to a previously encountered object class. These results are demonstrated on a mobile manipulator robot pushing a variety of household objects on a tabletop surface.

[1]  Mike Stilman,et al.  Combining motion planning and optimization for flexible robot manipulation , 2010, 2010 10th IEEE-RAS International Conference on Humanoid Robots.

[2]  James M. Rehg,et al.  Decoupling behavior, perception, and control for autonomous learning of affordances , 2013, 2013 IEEE International Conference on Robotics and Automation.

[3]  S. Srinivasa,et al.  Push-grasping with dexterous hands: Mechanics and a method , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Matthew T. Mason,et al.  Mechanics and Planning of Manipulator Pushing Operations , 1986 .

[5]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[6]  Danijel Skocaj,et al.  Self-supervised cross-modal online learning of basic object affordances for developmental robotic systems , 2010, 2010 IEEE International Conference on Robotics and Automation.

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

[8]  Kevin M. Lynch,et al.  Stable Pushing: Mechanics, Controllability, and Planning , 1995, Int. J. Robotics Res..

[9]  Yun Jiang,et al.  Learning to place new objects , 2011, 2012 IEEE International Conference on Robotics and Automation.

[10]  Ales Ude,et al.  Autonomous acquisition of pushing actions to support object grasping with a humanoid robot , 2009, 2009 9th IEEE-RAS International Conference on Humanoid Robots.

[11]  Rustam Stolkin,et al.  Learning to predict how rigid objects behave under simple manipulation , 2011, 2011 IEEE International Conference on Robotics and Automation.

[12]  Siddhartha S. Srinivasa,et al.  A Framework for Push-Grasping in Clutter , 2011, Robotics: Science and Systems.

[13]  Danica Kragic,et al.  Learning grasping points with shape context , 2010, Robotics Auton. Syst..

[14]  Andrew Zisserman,et al.  Multiple kernels for object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[15]  Quoc V. Le,et al.  Learning to grasp objects with multiple contact points , 2010, 2010 IEEE International Conference on Robotics and Automation.

[16]  Akansel Cosgun,et al.  Push planning for object placement on cluttered table surfaces , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  Gordon Cheng,et al.  Fast adaptation for effect-aware pushing , 2011, 2011 11th IEEE-RAS International Conference on Humanoid Robots.

[18]  Sundar Narasimhan,et al.  Task level strategies for robots , 1994 .

[19]  Giulio Sandini,et al.  A Vision-Based Learning Method for Pushing Manipulation , 1993 .

[20]  James M. Rehg,et al.  Learning stable pushing locations , 2013, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL).

[21]  Emre Ugur,et al.  Self-discovery of motor primitives and learning grasp affordances , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Alex Smola,et al.  Kernel methods in machine learning , 2007, math/0701907.