Researchers from ISIR, Sorbonne University, and the Gulliver laboratory, ESPCI Paris – PSL, have designed robots that move in or against the direction of the forces to which they are subjected, in particular during collisions, and this according to their morphology only. This work comes from the “Morphofunctional Swarm Robotics” project (project ANR-18-CE33-0006) and is presented in an article published in the journal Science Robotics published on February 22, 2023.

A swarm of robots makes it possible to accomplish tasks requiring cooperation between several individuals. To date, robot swarms are designed to operate exclusively in a dilute environment, i.e. avoiding collisions. However, this is not the case in living organisms, where great flexibility can be observed in both dilute and very dense environments, whether for cells, colonies of bacteria or ants, schools of fish, flocks of birds, or even humans. This suggests that simple primitive behaviors are sufficient to obtain collective behaviors, including in dense environments. Physical interactions can thus be used to the advantage of a swarm, including if it is made up of robots. The study and exploitation of these physical interactions between robots is the subject of an article at the interface of robotics, computer science and physics published in Science Robotics on February 22, 2023 by researchers from the ESPCI Paris – PSL and Sorbonne University.

In this article, the authors take advantage of a generic mechanical response: the tendency of a particle to reorient itself in response to an external force. They reveal the importance of this morphological response in different robotic tasks, both for a single robot and for a swarm of robots. The authors show that the way robots react in the event of collisions depends on their body and allows them to align themselves with or against an external force such as, for example, another robot, an object or a wall. By understanding the effect of physical interactions between robots and their environment, it then becomes possible to define the collective behavior that will result from multiple collisions between robots in the swarm. Depending on the exoskeleton that dresses the robots, we can thus observe that a robot will align itself with an external force while another will oppose it. In the event of a collision, a robot can thus push, or slide along an obstacle or another robot, and this only thanks to the passive dynamics induced by its mechanical design.

The authors of this article have also shown that it is possible to learn to exploit interactions between robots to perform collective robotics tasks requiring cooperation, such as collective aggregation in a bright area of the environment. To do this, the authors describe a decentralized reinforcement learning algorithm inspired by social learning: robots close enough to each other exchange information concerning the most effective behavioral strategies to accomplish the task assigned to the swarm of robots. The best strategies thus spread from robot to robot, making it possible to make the best use of the physical properties and computing capacities of the robots in the swarm.

Authors (in order):

Caption of the picture: Cooperation between 64 Morphobots for a phototaxis task (search for light). The morphobots that are in the illuminated area form an aggregate that grows over time, exploiting the properties given by their morphologies. In the event of a collision, each Morphobot naturally reorients itself to face the object at the origin of the force suffered. The formation of an aggregate in the light zone simply results from a slowing down of the speed of movement of the Morphobots, which occurs if the light intensity exceeds a threshold learned over time. The morphology does the rest. ©Matan_Yah_Ben_Zion