QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation

David Blanco-Mulero*, Gökhan Alcan, Fares Abu-Dakka, Ville Kyrki

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference article in proceedingsScientificvertaisarvioitu

Abstrakti

Pre-defined manipulation primitives are widely used for cloth manipulation. However, cloth properties such as its stiffness or density can highly impact the performance of these primitives. Although existing solutions have tackled the parameterisation of pick and place locations, the effect of factors such as the velocity or trajectory of quasi-static and dynamic manipulation primitives has been neglected. Choosing appropriate values for these parameters is crucial to cope with the range of materials present in house-hold cloth objects. To address this challenge, we introduce the Quasi-Dynamic Parameterisable (QDP) method, which optimises parameters such as the motion velocity in addition to the pick and place positions of quasi-static and dynamic manipulation primitives.
In this work, we leverage the framework of Sequential Reinforcement Learning to decouple sequentially the parameters that compose the primitives. To evaluate the effectiveness of the method we focus on the task of cloth unfolding with a robotic arm in simulation and real-world experiments. Our results in simulation show that by deciding the optimal parameters for the primitives the performance can improve by 20% compared to sub-optimal ones. Real-world results demonstrate the advantage of modifying the velocity and height of manipulation primitives for cloths with different mass, stiffness, shape and size. Supplementary material, videos, and code, can be found at https://sites.google.com/view/qdp-srl.
AlkuperäiskieliEnglanti
Otsikko2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
KustantajaIEEE
Sivut984-991
Sivumäärä8
ISBN (elektroninen)978-1-6654-9190-7
DOI - pysyväislinkit
TilaJulkaistu - 13 jouluk. 2023
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIEEE/RSJ International Conference on Intelligent Robots and Systems
- Detroit, Yhdysvallat
Kesto: 1 lokak. 20235 lokak. 2023

Julkaisusarja

NimiProceedings of the International Conference on Intelligent Robots and Systems
ISSN (elektroninen)2153-0866

Conference

ConferenceIEEE/RSJ International Conference on Intelligent Robots and Systems
LyhennettäIROS
Maa/AlueYhdysvallat
KaupunkiDetroit
Ajanjakso01/10/202305/10/2023

Sormenjälki

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