This document summarizes a journal club discussion on a paper about morphogenesis in robot swarms. The paper demonstrated self-organizing behaviors in a large swarm of 300 real robots that created emergent morphologies through local interactions alone. The swarm behaviors were adaptable to the number of robots and initial morphology, and the morphologies were robust to minor and major damage. The system demonstrated scalability and how physical shape change and molecular patterning can influence each other through feedback during morphogenesis.

1. 19/01/10 Journal club
Kazuya Horibe

2. Why did I choose the paper?
Bio-Inspired Self-Organising Multi-Robot system
• Flocking • Organ growth
Only local interaction!!
one Individual = one robot one cell = one robot
Most research work has been performed in simulations only with an approximated
model of sensors and robots.
Their goal is adaptive and robust system.

3. Real world Issues
• Robot cost
• Robot’s interaction with each other and with the environment
• Sensing noise and disturbance from environments
• Limited memory capacity of a robot
• Wireless communication network
• Communication delay, Packet loss, inconsistent connection
• Scaling issues
• Swarm behaviors using only a small number (~10) of robots

4. Kilobot (2011)
Kilobot is low cost while having enough functionality to
perform an array of collective tasks.
For self-assemble and self-heal a collective shape
• Forward movement
• Rotation
• Communication with nearby units
• Measure the distance between nearby units
• Enough memory to run them

5. Breakthrough in number of robots
2014 Programed behavior1024 kilobots

6. The contribution of this paper
“we provide a demonstration of purely self-
organizing behaviors to create emergent
morphologies in large swarms of real robots.”
~300
Only local interaction
Real world Issues

7. Setting; Multi cell organ growth
Reaction-diffusion system Cell migration
Neglect apoptosis and cell division

8. Result: Over view

9. Result①：Emergence of swarm
morphologies
Computer simulation
for parameter turning
Real robot system
Conceptual execution of
the swarm morphogenesis

10. Result②：Adaptability of swarm
morphologies
Adaptability to
number of robots
Adaptability to
initial morphology
(i) tentacle growth
(ii) shit trough the
tissue
Tip extensionMoving turing pattern
Four variable swarm
morphologies with
irregular

11. Result③：Robustness of swarm
morphologies
Regeneration
response to
minor damage.
Redirected
tentacle growth
response.
Regeneration
response to
major damage.

12. Morph space of swarm robots
9track
Disc = 1
The NSCP is
calculated
by counting the
number of a
polygon’s vertices.

13. Summary
• A large swarm of 300 robots with a self-organized
morphogenetic behavior
• On the one hand, the molecular patterning
process drives the physical shape change; on the
other hand, changes to the shape feedback cause
the molecular pattern to change.
• All swarms moved in a consistent direction
through morphospace
• This system has scalability.

14. Reference
• [Slavkov 2018] Slavkov, I., et al. "Morphogenesis in robot swarms." Science
Robotics 3.25 (2018): eaau9178.
• [Oh 2017] Oh, Hyondong, et al. "Bio-inspired self-organising multi-robot pattern
formation: A review." Robotics and Autonomous Systems91 (2017): 83-100.
• [Pfeifer & Bongard 2006] Pfeifer, Rolf, and Josh Bongard. How the body shapes
the way we think: a new view of intelligence. MIT press, 2006.
• [René, Sayama & Michel 2012] Doursat, René, Hiroki Sayama, and Olivier
Michel, eds. Morphogenetic engineering: toward programmable complex
systems. Springer, 2012.
• [Dietmar 2002] Moser, Dietmar, et al. "Landscape patch shape complexity as an
effective measure for plant species richness in rural landscapes." Landscape
Ecology 17.7 (2002): 657-669.