Research/ ORBITAL ROBOTICS
Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder
Clément Rolinat, Mathieu Grossard, Saifeddine Aloui, Christelle Godin
Manipulation & GraspingOpenAlex topic: Robot Manipulation and Learning · Learning-based MethodsOpenAlex topic: Reinforcement Learning in Robotics · Design, Mechanisms & HardwareOpenAlex topic: Soft Robotics and Applications
arXivcs.ROOpenAlexRobot Manipulation and Learning · Reinforcement Learning in Robotics · Soft Robotics and Applications
Grasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space.
Comments: accepted at SYSID 2021 conference
Journal ref: IFAC-PapersOnLine, 19th IFAC Symposium on System Identification SYSID 2021, Volume 54, Issue 7, 2021, Pages 523-528, ISSN 2405-8963