Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations

Mohtasib, Abdalkarim, Ghalamzan Esfahani, Amir, Bellotto, Nicola and Cuayahuitl, Heriberto (2021) Neural Task Success Classifiers for Robotic Manipulation from Few Real Demonstrations. In: International Joint Conference on Neural Networks (IJCNN).

© 20xx 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.

Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


Robots learning a new manipulation task from a small amount of demonstrations are increasingly demanded in different workspaces. A classifier model assessing the quality of actions can predict the successful completion of a task, which can be used by intelligent agents for action-selection. This paper presents a novel classifier that learns to classify task completion only from a few demonstrations. We carry out a comprehensive comparison of different neural classifiers, e.g. fully connected-based, fully convolutional-based, sequence2sequence-based, and domain adaptation-based classification. We also present a new dataset including five robot manipulation tasks, which is publicly available. We compared the performances of our novel classifier and the existing models using our dataset and the MIME dataset. The results suggest domain adaptation and timing-based features improve success prediction. Our novel model, i.e. fully convolutional neural network with domain adaptation and timing features, achieves an average classification accuracy of 97.3% and 95.5% across tasks in both datasets whereas state-of-the-art classifiers without domain adaptation and timing-features only achieve 82.4% and 90.3%, respectively.

Keywords:Deep Learning, Reward Learning, Task Success, Task Timing, Domain Adaptation, Robot Skill Learning
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
H Engineering > H671 Robotics
G Mathematical and Computer Sciences > G730 Neural Computing
Divisions:College of Science > School of Computer Science
ID Code:45559
Deposited On:27 Aug 2021 12:51

Repository Staff Only: item control page