Learning to assemble objects with a robot swarm

Large populations of simple robots can solve complex tasks, but controlling them is still a challenging problem, due to limited communication and computation power. In order to assemble objects, have shown that a human controller can solve such a task. Instead, we investigate how to learn the assembly of multiple objects with a single central controller. We propose splitting the assembly process in two sub-tasks -- generating a top-level assembly policy and learning an object movement policy. The assembly policy plans the trajectories for each object and the object movement policy controls the trajectory execution.The resulting system is able to solve assembly tasks with varying object shapes being assembled as shown in multiple simulation scenarios

Social competence improves the performance of biomimetic robots leading live fish

Collective motion is commonly modeled with static interaction rules between agents. Substantial empirical evidence indicates, however, that animals may adapt their interaction rules depending on a variety of factors and social contexts. Here, we hypothesized that leadership performance is linked to the leader's responsiveness to the follower's actions and we predicted that a leader is followed longer if it adapts to the follower's avoidance movements. We tested this prediction with live guppies that interacted with a biomimetic robotic fish programmed to act as a 'socially competent' leader. Fish that were avoiding the robot were approached more carefully in future approaches. In two separate experiments we then asked how the leadership performance of the socially competent robot leader differed to that of a robot leader that either approached all fish in the same, non-responsive, way or one that did change its approach behavior randomly, irrespective of the fish's actions. We found that (1) behavioral variability itself appears attractive and that socially competent robots are better leaders which (2) require fewer approach attempts to (3) elicit longer average following behavior than non-competent agents. This work provides evidence that social responsiveness to avoidance reactions plays a role in the social dynamics of guppies. We showcase how social responsiveness can be modeled and tested directly embedded in a living animal model using adaptive, interactive robots

Animal-in-the-Loop: Using Interactive Robotic Conspecifics to Study Social Behavior in Animal Groups

Biomimetic robots that replace living social interaction partners can help elucidate the underlying interaction rules in animal groups. Our review focuses on the use of interactive robots that respond dynamically to animal behavior as part of a closed control loop. We discuss the most influential works to date and how they have contributed to our understanding of animal sociality. Technological advances permit the use of robots that can adapt to the situations they face and the conspecifics they encounter, or robots that learn to optimize their social performance from a set of experiences. We discuss how adaptation and learning may provide novel insights into group sociobiology and describe the technical challenges associated with these types of interactive robots. This interdisciplinary field provides a rich set of problems to be tackled by roboticists, machine learning engineers, and control theorists. By cultivating smarter robots, we can usher in an era of more nuanced exploration of animal behavior