DexDLO: Learning Goal-Conditioned Dexterous Policy for Dynamic Manipulation of Deformable Linear Objects

Zhaole Sun, Jihong Zhu, Robert B. Fisher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Deformable linear object (DLO) manipulation is needed in many fields. Previous research on deformable linear object (DLO) manipulation has primarily involved parallel jaw gripper manipulation with fixed grasping positions. However, the potential for dexterous manipulation of DLOs using an anthropomorphic hand is under-explored. We present DexDLO, a model-free framework that learns dexterous dynamic manipulation policies for deformable linear objects with a fixed base dexterous hand in an end-to-end way. By abstracting several common DLO manipulation tasks into goal-conditioned tasks, DexDLO can perform tasks such as DLO grabbing, DLO pulling,DLO end-tip position controlling, etc. Using the Mujoco physics simulator, we demonstrate that our framework can efficiently and effectively learn five different DLO manipulation tasks with the same framework parameters. We further provide a thorough analysis of learned policies, reward functions, and reduced observations for a comprehensive understanding of the framework.
Original languageEnglish
Title of host publication2024 IEEE International Conference on Robotics and Automation (ICRA)
PublisherIEEE
Pages1-12
Number of pages12
Publication statusAccepted/In press - 29 Jan 2024
Event2024 IEEE International Conference on Robotics and Automation - Yokohama, Japan
Duration: 13 May 202417 May 2024
https://2024.ieee-icra.org/

Conference

Conference2024 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2024
Country/TerritoryJapan
CityYokohama
Period13/05/2417/05/24
Internet address

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