Multi-Instance Aware Localization for End-to-End Imitation Learning

TL;DR

This paper introduces a novel end-to-end imitation learning architecture that improves multi-instance object localization and control, achieving high accuracy and sample efficiency with limited demonstrations in robotic tasks.

Contribution

It proposes a new architecture combining feature map embeddings and autoregressive control for better multi-instance localization in imitation learning.

Findings

Improved localization accuracy in multi-instance scenarios.

Achieved effective training with as few as 15 demonstrations.

Generalized to unseen object instances during testing.

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.