Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Active Matter as a framework for living systems-inspired Robophysics

Giulia Janzen, Gaia Maselli, Juan F. Jimenez, Lia Garcia-Perez, D A Matoz Fernandez, Chantal Valeriani

TL;DR

This paper surveys how robophysics can be grounded in active-matter physics to understand and design living-system-inspired robotic collectives. It analyzes single-robot locomotion and the challenges of swarm-scale coordination, emphasizing local interaction rules, communication constraints, and scalable testing platforms. The authors argue that active matter provides the minimal ingredients for life-like collective motion, and advocate integrating perception, adaptability, and goal-directed behavior through machine learning and reinforcement learning to realize purposeful swarms. The work highlights the need for interdisciplinary collaboration across physics, robotics, and computer science to move from locomotion to programmable, energy-aware swarms capable of robust performance in complex environments.

Abstract

Robophysics investigates the physical principles that govern living-like robots operating in complex, realworld environments. Despite remarkable technological advances, robots continue to face fundamental efficiency limitations. At the level of individual units, locomotion remains a challenge, while at the collective level, robot swarms struggle to achieve shared purpose, coordination, communication, and cost efficiency. This perspective article examines the key challenges faced by bio-inspired robotic collectives and highlights recent research efforts that incorporate principles from active-matter physics and biology into the modeling and design of robot swarms.

Active Matter as a framework for living systems-inspired Robophysics

TL;DR

This paper surveys how robophysics can be grounded in active-matter physics to understand and design living-system-inspired robotic collectives. It analyzes single-robot locomotion and the challenges of swarm-scale coordination, emphasizing local interaction rules, communication constraints, and scalable testing platforms. The authors argue that active matter provides the minimal ingredients for life-like collective motion, and advocate integrating perception, adaptability, and goal-directed behavior through machine learning and reinforcement learning to realize purposeful swarms. The work highlights the need for interdisciplinary collaboration across physics, robotics, and computer science to move from locomotion to programmable, energy-aware swarms capable of robust performance in complex environments.

Abstract

Robophysics investigates the physical principles that govern living-like robots operating in complex, realworld environments. Despite remarkable technological advances, robots continue to face fundamental efficiency limitations. At the level of individual units, locomotion remains a challenge, while at the collective level, robot swarms struggle to achieve shared purpose, coordination, communication, and cost efficiency. This perspective article examines the key challenges faced by bio-inspired robotic collectives and highlights recent research efforts that incorporate principles from active-matter physics and biology into the modeling and design of robot swarms.

Paper Structure

This paper contains 12 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Learning from living systems: single robot
  3. Learning from living systems: collective behaviour
  4. Communication and its constraints
  5. Coordination and Scalability
  6. Cooperation, routing and planning
  7. Collaboration, Learning and Adaptation
  8. Competition and a shared purpose
  9. Testing robot swarms
  10. Active Matter as an effective framework for Robophysics
  11. Outlook
  12. Acknowledgments

Figures (2)

  • Figure 1: Schematic illustration of how principles from living systems can inform robophysics. In living systems (top), groups of organisms such as fish, ants, and birds display collective behavior, adaptability, and shared purpose. These principles inspire robotic collectives (bottom), where multi-robot swarms must address challenges such as communication, perception, and sensing, and energy and resource management.
  • Figure 2: Group of five differential robots from the Robotarium-UCM.