In this work, the robotic manipulation of a highly Deformable Linear Object (DLO) is addressed by means of a sequence of pick-and-drop primitives driven by visual data. A decision making process learns the optimal grasping location exploiting deep Q-learning and finds the best releasing point from a path representation of the DLO shape. The system effectively combines a state-of-the-art algorithm for semantic segmentation specifically designed for DLOs with deep reinforcement learning. Experimental results show that our system is capable to manipulate a DLO into a variety of different shapes in few steps. The intermediate steps of deformation that lead the object from its initial configuration to the target one are also provided and analyzed.

Zanella R., Palli G. (2021). Robot Learning-Based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds. IEEE ACCESS, 9, 138296-138306 [10.1109/ACCESS.2021.3118209].

Robot Learning-Based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds

Zanella R.
Investigation
;
Palli G.
Supervision
2021

Abstract

In this work, the robotic manipulation of a highly Deformable Linear Object (DLO) is addressed by means of a sequence of pick-and-drop primitives driven by visual data. A decision making process learns the optimal grasping location exploiting deep Q-learning and finds the best releasing point from a path representation of the DLO shape. The system effectively combines a state-of-the-art algorithm for semantic segmentation specifically designed for DLOs with deep reinforcement learning. Experimental results show that our system is capable to manipulate a DLO into a variety of different shapes in few steps. The intermediate steps of deformation that lead the object from its initial configuration to the target one are also provided and analyzed.
2021
Zanella R., Palli G. (2021). Robot Learning-Based Pipeline for Autonomous Reshaping of a Deformable Linear Object in Cluttered Backgrounds. IEEE ACCESS, 9, 138296-138306 [10.1109/ACCESS.2021.3118209].
Zanella R.; Palli G.
File in questo prodotto:
File Dimensione Formato  
Robot_Learning-Based_Pipeline_for_Autonomous_Reshaping_of_a_Deformable_Linear_Object_in_Cluttered_Backgrounds.pdf

accesso aperto

Tipo: Versione (PDF) editoriale
Licenza: Licenza per Accesso Aperto. Creative Commons Attribuzione (CCBY)
Dimensione 2.98 MB
Formato Adobe PDF
2.98 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/11585/841312
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 3
  • ???jsp.display-item.citation.isi??? 1
social impact