Imitation learning techniques have been used as a way to transfer skills to robots. Among them, dynamic movement primitives (DMPs) have been widely exploited as an effective and an efficient technique to learn and reproduce complex discrete and periodic skills. While DMPs have been properly formulated for learning point-to-point movements for both translation and orientation, periodic ones are missing a formulation to learn the orientation. To address this gap, we propose a novel DMP formulation that enables encoding of periodic orientation trajectories. Within this formulation we develop two approaches: Riemannian metric-based projection approach and unit quaternion based periodic DMP. Both formulations exploit unit quaternions to represent the orientation. However, the first exploits the properties of Riemannian manifolds to work in the tangent space of the unit sphere. The second encodes directly the unit quaternion trajectory while guaranteeing the unitary norm of the generated quaternions. We validated the technical aspects of the proposed methods in simulation. Then we performed experiments on a real robot to execute daily tasks that involve periodic orientation changes (i.e., surface polishing/wiping and liquid mixing by shaking).

Periodic DMP formulation for Quaternion Trajectories / Abu-Dakka, Fares J.; Saveriano, Matteo; Peternel, Luka. - (2021), pp. 658-663. (Intervento presentato al convegno ICAR 2021 tenutosi a Ljubljana nel 6th-10th December 2021) [10.1109/ICAR53236.2021.9659319].

Periodic DMP formulation for Quaternion Trajectories

Saveriano, Matteo
Secondo
;
2021-01-01

Abstract

Imitation learning techniques have been used as a way to transfer skills to robots. Among them, dynamic movement primitives (DMPs) have been widely exploited as an effective and an efficient technique to learn and reproduce complex discrete and periodic skills. While DMPs have been properly formulated for learning point-to-point movements for both translation and orientation, periodic ones are missing a formulation to learn the orientation. To address this gap, we propose a novel DMP formulation that enables encoding of periodic orientation trajectories. Within this formulation we develop two approaches: Riemannian metric-based projection approach and unit quaternion based periodic DMP. Both formulations exploit unit quaternions to represent the orientation. However, the first exploits the properties of Riemannian manifolds to work in the tangent space of the unit sphere. The second encodes directly the unit quaternion trajectory while guaranteeing the unitary norm of the generated quaternions. We validated the technical aspects of the proposed methods in simulation. Then we performed experiments on a real robot to execute daily tasks that involve periodic orientation changes (i.e., surface polishing/wiping and liquid mixing by shaking).
2021
2021 20th International Conference on Advanced Robotics
Piscataway, New Jersey, US
Institute of Electrical and Electronics Engineers Inc.
978-1-6654-3684-7
Abu-Dakka, Fares J.; Saveriano, Matteo; Peternel, Luka
Periodic DMP formulation for Quaternion Trajectories / Abu-Dakka, Fares J.; Saveriano, Matteo; Peternel, Luka. - (2021), pp. 658-663. (Intervento presentato al convegno ICAR 2021 tenutosi a Ljubljana nel 6th-10th December 2021) [10.1109/ICAR53236.2021.9659319].
File in questo prodotto:
File Dimensione Formato  
Periodic_DMP_formulation_for_Quaternion_Trajectories.pdf

Solo gestori archivio

Tipologia: Versione editoriale (Publisher’s layout)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 4.37 MB
Formato Adobe PDF
4.37 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/330172
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 7
social impact