A Robot to Shape your Natural Plant: The Machine Learning Approach to Model and Control Bio-Hybrid Systems

Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significan...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-04
Main Authors: Wahby, Mostafa, Heinrich, Mary Katherine, Daniel Nicolas Hofstadler, Zahadat, Payam, Risi, Sebastian, Ayres, Phil, Schmickl, Thomas, Hamann, Heiko
Format: Article
Language:English
Subjects:
Artificial intelligence
Computer vision
Controllers
Couplings
Decision making
Evolution
Hybrid systems
Machine learning
Neural networks
Obstacle avoidance
Plants (botany)
Robot control
Robot dynamics
Robots
Stiffening
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Bio-hybrid systems---close couplings of natural organisms with technology---are high potential and still underexplored. In existing work, robots have mostly influenced group behaviors of animals. We explore the possibilities of mixing robots with natural plants, merging useful attributes. Significant synergies arise by combining the plants' ability to efficiently produce shaped material and the robots' ability to extend sensing and decision-making behaviors. However, programming robots to control plant motion and shape requires good knowledge of complex plant behaviors. Therefore, we use machine learning to create a holistic plant model and evolve robot controllers. As a benchmark task we choose obstacle avoidance. We use computer vision to construct a model of plant stem stiffening and motion dynamics by training an LSTM network. The LSTM network acts as a forward model predicting change in the plant, driving the evolution of neural network robot controllers. The evolved controllers augment the plants' natural light-finding and tissue-stiffening behaviors to avoid obstacles and grow desired shapes. We successfully verify the robot controllers and bio-hybrid behavior in reality, with a physical setup and actual plants.
ISSN:2331-8422
DOI:10.48550/arxiv.1804.06682