Learning Relative Interactions through Imitation

• URL: http://arxiv.org/abs/2109.12013v1
• Date: Fri, 24 Sep 2021 15:18:34 GMT
• Title: Learning Relative Interactions through Imitation
• Authors: Giorgia Adorni and Elia Cereda
• Abstract summary: We show that a simple network, with relatively little training data, is able to reach very good performance on the fixed-pose task. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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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