Paper Info
Title
Robotic bimanual cleaning of deformable objects with online learning of part and tool models
Abstract
In this paper, we present an approach to perform automatic robotic cleaning of deformable objects with unknown stiffness characteristics. A bimanual robot setup is used, where one arm holds the part to be cleaned, while the other holds the cleaning tool. The robot maintains an approximate model of the deformation behavior of each part it interacts with and incrementally improves the model as it performs cleaning attempts, thereby gaining information. Simultaneously, the robot maintains a model of the cleaning tool performance which is independent of the particular part and can be learned over multiple episodes of interaction with different parts. During each attempt, the robot exploits its current knowledge of the part deformation behavior to select an optimal set of grasp locations that minimize the amount of deformation. Results indicate the system is able to incrementally learn the deformation model of parts with approximate linear geometry and that the improving model can be quickly used to select the correct grasp locations and tool parameters for rapid cleaning.
Year
DOI
Venue
2016
10.1109/COASE.2016.7743460
2016 IEEE International Conference on Automation Science and Engineering (CASE)
Keywords
Field
DocType
automatic robotic cleaning,deformable objects,unknown stiffness characteristics,robotic bimanual cleaning,bimanual robot setup,cleaning tool,deformation behavior,cleaning tool performance,grasp locations,approximate linear geometry,tool parameters
Online learning,Computer vision,GRASP,Stiffness,Robot kinematics,Exploit,Artificial intelligence,Deformation (mechanics),Engineering,Robot,Trajectory
Conference
ISBN
Citations 
PageRank 
978-1-5090-2410-0
3
0.40
References 
Authors
11
4
Name
Order
Citations
PageRank
Joshua D. Langsfeld1163.32
Ariyan M. Kabir2186.94
Krishnanand N. Kaipa38110.88
Satyandra K Gupta468777.11