A Data-Driven Statistical Framework for Post-Grasp Manipulation

Robert Paolini, Alberto Rodriguez, Siddhartha Srinivasa and Matthew T. Mason
Journal Article, International Journal of Robotics Research (IJRR), Vol. 33, No. 4, pp. 600-615, April, 2014

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Grasping an object is usually only an intermediate goal for a robotic manipulator. To finish the task, the robot needs to know where the object is in its hand and what action to execute. This paper presents a general statistical framework to address these problems. Given a novel object, the robot learns a statistical model of grasp state conditioned on sensor values. The robot also builds a statistical model of the requirements for a successful execution of the task in terms of uncertainty in the state of the grasp. Both of these models are constructed by offline experiments. The online process then grasps objects and chooses actions to maximize likelihood of success. This paper describes the framework in detail, and demonstrates its effectiveness experimentally in placing, dropping, and insertion tasks. To construct statistical models, the robot performed over 8000 grasp trials, and over 1000 trials each of placing, dropping and insertion.

author = {Robert Paolini and Alberto Rodriguez and Siddhartha Srinivasa and Matthew T. Mason},
title = {A Data-Driven Statistical Framework for Post-Grasp Manipulation},
journal = {International Journal of Robotics Research (IJRR)},
year = {2014},
month = {April},
volume = {33},
number = {4},
pages = {600-615},
} 2019-06-27T13:08:25-04:00