Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder
This work addresses grasp planning for known objects in robotics, but it is incremental as it applies existing VAE techniques to a specific robotic domain.
The authors tackled the problem of grasp space exploration for a multi-fingered adaptive gripper by developing a data-driven method using a variational autoencoder to learn grasp features from a limited expert dataset, enabling the generation of new gripper configurations.
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.