GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping

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GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping
Title:
GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping
Other Titles:
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Keywords:
Publication Date:
16 December 2021
Citation:
Kurkcu, A., Acar, C., Campolo, D., & Tee, K. P. (2021). GloCAL: Glocalized Curriculum-Aided Learning of Multiple Tasks with Application to Robotic Grasping. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). https://doi.org/10.1109/iros51168.2021.9636492
Abstract:
The domain of robotics is challenging to apply deep reinforcement learning due to the need for large amounts of data and for ensuring safety during learning. Curriculum learning has shown good performance in terms of sample-efficient deep learning. In this paper, we propose an algorithm (named GloCAL) that creates a curriculum for an agent to learn multiple discrete tasks, based on clustering tasks according to their evaluation scores. From the highest-performing cluster, a global task representative of the cluster is identified for learning a global policy that transfers to subsequently formed new clusters, while remaining tasks in the cluster are learnt as local policies. The efficacy and efficiency of our GloCAL algorithm are compared with other approaches in the domain of grasp learning for 49 objects with varied object complexity and grasp difficulty from the EGAD! dataset. The results show that GloCAL is able to learn to grasp 100% of the objects, whereas other approaches achieve at most 86% despite being given 1.5× longer training time.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - RIE 2020 plan (Advanced Manufacturing and Engineering domain)
Grant Reference no. : A19E4a0101
Description:
© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
ISSN:
978-1-6654-1714-3
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