Title
Enhancing robotic unstructured bin-picking performance by enabling remote human interventions in challenging perception scenarios
Abstract
We present an approach that enables a robot to initiate a call to a remote human operator and ask help in resolving automated perception system failures during bin-picking operations. Our approach allows a robot to evaluate the quality of part recognition and pose estimation, based on a confidence-measure, and thereby determine whether to proceed with the task execution or to request assistance from a human in resolving the predicted perception failure. We present an automated perception algorithm that performs the joint task of part recognition and 6 degree-of-freedom pose estimation, and has built-in features to initiate the call to the human when needed. We also present the underlying mechanism for a rationalized basis for making the call to the human. If uncertainty in part detection leads to perception failure, then human intervention is invoked. We present a new user interface that enables remote human interventions when necessary. We report results from experiments with a dual-armed Baxter robot to validate our approach.
Year
DOI
Venue
2016
10.1109/COASE.2016.7743462
2016 IEEE International Conference on Automation Science and Engineering (CASE)
Keywords
Field
DocType
robotic unstructured bin-picking performance,remote human intervention,automated perception system failure,bin-picking operation,part recognition,pose estimation,confidence measure,automated perception algorithm,part detection,user interface,dual-armed Baxter robot
Psychological intervention,Ask price,Pose,Human–computer interaction,Artificial intelligence,Solid modeling,Engineering,Robot,User interface,Perception,Human–robot interaction
Conference
ISBN
Citations 
PageRank 
978-1-5090-2410-0
1
0.36
References 
Authors
19
7