Open AccessContinuous learning of emergent behavior in robotic matter
Author(s) -
Giorgio Oliveri,
Lucas C. van Laake,
Cesare Carissimo,
Clara Miette,
Johannes T. B. Overvelde
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2017015118
Subject(s) - modular design , scalability , robot , computer science , distributed computing , artificial intelligence , controller (irrigation) , robotics , self reconfiguring modular robot , human–computer interaction , control engineering , mobile robot , robot control , engineering , biology , database , agronomy , operating system
Significance In the last century, robots have been revolutionizing our lives, augmenting human actions with greater precision and repeatability. Unfortunately, most robotic systems can only operate in controlled environments. While increasing the complexity of the centralized controller is an instinctive direction to enable robots that are capable of autonomously adapting to their environment, there are ample examples in nature where adaptivity emerges from simpler decentralized processes. Here we perform experiments and simulations on a modular and scalable robotic platform in which each unit is stochastically updating its own behavior to explore requirements needed for a decentralized learning strategy capable of achieving locomotion in a continuously changing environment or when undergoing damage.