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Nature. 2019 Mar;567(7748):361-365. doi: 10.1038/s41586-019-1022-9. Epub 2019 Mar 20.

Particle robotics based on statistical mechanics of loosely coupled components.

Author information

1
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. lisg@csail.mit.edu.
2
Creative Machines Laboratory, Mechanical Engineering Department, Columbia University, New York, NY, USA. lisg@csail.mit.edu.
3
Creative Machines Laboratory, Mechanical Engineering Department, Columbia University, New York, NY, USA. richa.batra@columbia.edu.
4
School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY, USA.
5
Graduate School of Design, Harvard University, Cambridge, MA, USA.
6
Wyss Institute for Biologically Inspired Engineering, Harvard University, Cambridge, MA, USA.
7
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
8
Creative Machines Laboratory, Mechanical Engineering Department, Columbia University, New York, NY, USA. hod.lipson@columbia.edu.

Abstract

Biological organisms achieve robust high-level behaviours by combining and coordinating stochastic low-level components1-3. By contrast, most current robotic systems comprise either monolithic mechanisms4,5 or modular units with coordinated motions6,7. Such robots require explicit control of individual components to perform specific functions, and the failure of one component typically renders the entire robot inoperable. Here we demonstrate a robotic system whose overall behaviour can be successfully controlled by exploiting statistical mechanics phenomena. We achieve this by incorporating many loosely coupled 'particles', which are incapable of independent locomotion and do not possess individual identity or addressable position. In the proposed system, each particle is permitted to perform only uniform volumetric oscillations that are phase-modulated by a global signal. Despite the stochastic motion of the robot and lack of direct control of its individual components, we demonstrate physical robots composed of up to two dozen particles and simulated robots with up to 100,000 particles capable of robust locomotion, object transport and phototaxis (movement towards a light stimulus). Locomotion is maintained even when 20 per cent of the particles malfunction. These findings indicate that stochastic systems may offer an alternative approach to more complex and exacting robots via large-scale robust amorphous robotic systems that exhibit deterministic behaviour.

PMID:
30894722
DOI:
10.1038/s41586-019-1022-9

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