Learning Robotic Manipulation of Natural Materials With Variable Properties for Construction Tasks

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 ro...

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Vydáno v:IEEE robotics and automation letters Ročník 7; číslo 2; s. 5749 - 5756
Hlavní autoři: Kalousdian, Nicolas Kubail, Lochnicki, Grzegorz, Hartmann, Valentin N., Leder, Samuel, Oguz, Ozgur S., Menges, Achim, Toussaint, Marc
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 01.04.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
AI-enabled robotics
Assembly
Automation
Bamboo
Bending
Curricula
Hardware
hardware-software integration in robotics
Machine learning
Manufacturing engineering
Mechanical properties
Mobile robots
Robotics
robotics and automation in construction
Robots
Task analysis
Transportation
ISSN:2377-3766, 2377-3766
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Shrnutí: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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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2022.3159288