Learning robotic manipulation of natural materials with variable properties for construction tasks

Date
2022-03-15
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Source Title
IEEE Robotics and Automation Letters
Print ISSN
Electronic ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers
Volume
7
Issue
2
Pages
5749 - 5756
Language
English
Type
Article
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Abstract

The introduction of robotics and machine learning to architectural construction is leading to more efficient construction practices. So far, robotic construction has largely been implemented on standardized materials, conducting simple, predictable, and repetitive tasks. We present a novel mobile robotic system and corresponding learning approach that takes a step towards assembly of natural materials with anisotropic mechanical properties for more sustainable architectural construction. Through experiments both in simulation and in the real world, we demonstrate a dynamically adjusted curriculum and randomization approach for the problem of learning manipulation tasks involving materials with biological variability, namely bamboo. Using our approach, robots are able to transport bamboo bundles and reach to goal-positions during the assembly of bamboo structures.

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Keywords
AI-enabled robotics, Hardware-software, Integration in robotics, Robotics and automation in construction
Citation
Published Version (Please cite this version)