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arXiv · 2109.12013

Learning Relative Interactions through Imitation

Giorgia Adorni·Elia Cereda
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Abstract

In this project we trained a neural network to perform specific interactions between a robot and objects in the environment, through imitation learning. In particular, we tackle the task of moving the robot to a fixed pose with respect to a certain object and later extend our method to handle any arbitrary pose around this object. We show that a simple network, with relatively little training data, is able to reach very good performance on the fixed-pose task, while more work is needed to perform the arbitrary-pose task satisfactorily. We also explore the effect of ambiguities in the sensor readings, in particular caused by symmetries in the target object, on the behaviour of the learned controller.

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Giorgia Adorni, Elia Cereda. 2021-09-24. Learning Relative Interactions through Imitation. https://arxiv.org/abs/2109.12013

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