A data-driven statistical framework for post-grasp manipulation

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 le...

Full description

Saved in:
Bibliographic Details
Published in:The International journal of robotics research 2014-04, Vol.33 (4), p.600-615
Main Authors: Paolini, Robert, Rodriguez, Alberto, Srinivasa, Siddhartha S., Mason, Matthew T.
Format: Article
Language:English
Subjects:
Construction
Grasping
Insertion
Mathematical models
Maximum likelihood method
Placing
Robotics
Robots
Sensors
Statistical analysis
Tasks
Uncertainty
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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 8,000 grasp trials, and over 1,000 trials each of placing, dropping, and insertion.
ISSN:0278-3649
1741-3176
DOI:10.1177/0278364913507756