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  4. Learning to Hit: A statistical Dynamical System based approach
 
conference paper

Learning to Hit: A statistical Dynamical System based approach

Khurana, Harshit  
•
Bombile Bosongo, Michael  
•
Billard, Aude  orcid-logo
2021
IEEE/RSJ International Conference on Intelligent Robots and Systems
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

This paper proposes a manipulation scheme based on learning the motion of objects after being hit by a robotic end-effector. This allows for the object to be positioned at a desired location outside the physical workspace of the robot. An estimate of the object dynamics under friction and collisions is learnt and used to predict the desired hitting parameters (speed and direction), given the initial and desired location of the object. Based on the obtained hitting parameters, the desired pre-impact velocity of the end-effector is generated using a stable dynamical system. The performance of the proposed DS is validated in simulation and and is used to learn a model for hitting using real robot. The approach is tested on real robot with a KUKA LBR IIWA robot.

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Type
conference paper
DOI
10.1109/IROS51168.2021.9635976
Author(s)
Khurana, Harshit  
Bombile Bosongo, Michael  
Billard, Aude  orcid-logo
Date Issued

2021

Publisher

IEEE

Published in
IEEE/RSJ International Conference on Intelligent Robots and Systems
Total of pages

7

Start page

9415

End page

9421

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LASA  
Event nameEvent placeEvent date
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Prague, Czech Republic (on-line)

Sept 27 - Oct 1, 2021

Available on Infoscience
August 13, 2021
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/180534
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