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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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Learning_to_Hit__A_statistical_Dynamical_System_based_approach_IROS_2021.pdf

Type

Postprint

Version

Accepted version

Access type

openaccess

License Condition

CC BY-NC

Size

3.96 MB

Format

Adobe PDF

Checksum (MD5)

f79ab5957ee3cba9075494b8a2688384

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