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 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.

Automated summary

The introduction of robotics and machine learning has improved efficiency in architectural construction. This study presents a new mobile robotic system and learning approach for assembling natural materials with varying mechanical properties, aiming to achieve sustainable construction practices.