Multi-instance aware localization for end-to-end imitation learning

Gubbi Venkatesh, S and Upadrashta, R and Kolathaya, S and Amrutur, B (2020) Multi-instance aware localization for end-to-end imitation learning. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020, 24 Oct 2020 - 24 Jan 2021, Las Vegas; United States, pp. 5225-5230.

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Abstract

Existing architectures for imitation learning using image-to-action policy networks perform poorly when presented with an input image containing multiple instances of the object of interest, especially when the number of expert demonstrations available for training are limited. We show that end-to-end policy networks can be trained in a sample efficient manner by (a) appending the feature map output of the vision layers with an embedding that can indicate instance preference or take advantage of an implicit preference present in the expert demonstrations, and (b) employing an autoregressive action generator network for the control layers. The proposed architecture for localization has improved accuracy and sample efficiency and can generalize to the presence of more instances of objects than seen during training. When used for end-to-end imitation learning to perform reach, push, and pick-and-place tasks on a real robot, training is achieved with as few as 15 expert demonstrations. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	IEEE International Conference on Intelligent Robots and Systems
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to Institute of Electrical and Electronics Engineers Inc.
Keywords:	Agricultural robots; Demonstrations; Intelligent robots; Network architecture, Action policies; Auto-regressive; Existing architectures; Imitation learning; Multiple instances; Pick and place; Policy networks; Proposed architectures, Educational robots
Department/Centre:	Division of Electrical Sciences > Electrical Communication Engineering Division of Interdisciplinary Sciences > Robert Bosch Centre for Cyber Physical Systems
Date Deposited:	26 Mar 2021 06:56
Last Modified:	26 Mar 2021 06:56
URI:	http://eprints.iisc.ac.in/id/eprint/68612

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