From RGB images to Dynamic Movement Primitives for planar tasks

DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patterns that take into account not only the initial and target position but also feature...

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Bibliographic Details
Published in2023 IEEE-RAS 22nd International Conference on Humanoid Robots (Humanoids) pp. 1 - 8
Main Authors Sidiropoulos, Antonis, Doulgeri, Zoe
Format Conference Proceeding
LanguageEnglish
Published IEEE 12.12.2023
Subjects
Dynamics
Humanoid robots
Layout
Pipelines
Robustness
Task analysis
Visualization
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Summary:DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patterns that take into account not only the initial and target position but also features relating to the overall structure and layout of the scene. To make DMP applicable to a wider range of tasks and further automate their use, we design a framework combining deep residual networks with DMP, that can encapsulate different motion patterns of a planar task, provided through human demonstrations on the RGB image plane. We can then automatically infer from new raw RGB visual input the appropriate DMP parameters, i.e. the weights that determine the motion pattern and the initial/target positions. We compare our method against another SoA method for inferring DMP from images and carry out experimental validations in two different planar tasks.
ISSN:2164-0580
DOI:10.1109/Humanoids57100.2023.10375217