Authors:
Rui Yang
1
;
2
;
Matthieu Grard
1
;
Emmanuel Dellandréa
2
and
Liming Chen
2
Affiliations:
1
Siléane, 17 Rue Descartes, Saint-Etienne, France
;
2
Liris, Ecole Centrale de Lyon, 36 Av. Guy de Collongue, Ecully, France
Keyword(s):
Robots-Grasp, Continual Learning.
Abstract:
Robotic grasp detection is to predict a grasp configuration, e.g., grasp location, gripper openness size, to enable a suitable end-effector to stably grasp a given object on the scene, whereas continual learning (CL) refers to the skill of an artificial learning system to learn continuously about the external changing world. Because it corresponds to real-life scenarios where data and tasks continuously occur, CL has aroused increasing interest in research communities. Numerous studies have focused so far on image classification, but none of them involve robotic grasp detection, although extending continuously robots with novel grasp capabilities when facing novel objects in unknown scenes is a major requirement of real-life applications. In this paper, we propose a first benchmark, namely Jacquard-CL, that uses a small part of the Jacquard Dataset with variations of the illumination and background to create a NI(new instances)-like scenario. Then, we adapt and benchmark several stat
e-of-the-art continual learning methods to the grasp detection problem and create a baseline for the issue of continual grasp detection. The experiments show that regularization-based methods struggle to retain the previously learned knowledge, but memory-based methods perform better.
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