Learning robotic manipulation of natural materials with variable properties for construction tasks
Author(s)
Date
2022-03-15Source Title
IEEE Robotics and Automation Letters
Electronic ISSN
2377-3766
Publisher
Institute of Electrical and Electronics Engineers
Volume
7
Issue
2
Pages
5749 - 5756
Language
English
Type
ArticleItem Usage Stats
4
views
views
6
downloads
downloads
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.
Keywords
AI-enabled roboticsHardware-software
Integration in robotics
Robotics and automation in construction