Effective foraging in a predictable local environment requires coordinating movement with the observable spatial context, in a word, navigation. Distinct from search, navigating to specific areas known to be valuable entails its own particularities. How space is understood through vision and parsed for navigation is often examined experimentally, with limited ability to manipulate sensory inputs and probe into the algorithmic level of decision-making. As a generalizable, minimal alternative to empirical means, embodied neural networks were evolved and studied to explore information processing algorithms an organism may use for visual spatial navigation. Surprisingly, three distinct classes of algorithms emerged, each with its own set of rules and tradeoffs, and each appear to be highly relevant to observable biological navigation behaviors.

The evolution of opinions in collectives is influenced by, and simultaneously influences, the interaction network. Simple rules like conformity and homophily drive the co-evolution of network and opinions, leading to the emergence of complex collective behaviors. Studying these behaviors gives insight into complex social dynamics, including the formation of echo chambers. This paper highlights how spatial information sources, and network connectivity shape echo chambers. We propose a potential solution to overcome the local trap of echo chambers by leveraging the mobility of agents.

Inter-individual differences are studied in natural systems, such as fish, bees, and humans, as they contribute to the complexity of both individual and collective behaviors. However, individuality in artificial systems, such as robotic swarms, is undervalued or even overlooked. Agent-specific deviations from the norm in swarm robotics are usually understood as mere noise that can be minimized, for example, by calibration. We observe that robots have consistent deviations and argue that awareness and knowledge of these can be exploited to serve a task. We measure heterogeneity in robot swarms caused by individual differences in how robots act, sense, and oscillate. Our use case is Kilobots and we provide example behaviors where the performance of robots varies depending on individual differences. We show a non-intuitive example of phototaxis with Kilobots where the non-calibrated Kilobots show better performance than the calibrated supposedly “ideal” one. We measure the inter-individual variations for heterogeneity in sensing and oscillation, too. We briefly discuss how these variations can enhance the complexity of collective behaviors. We suggest that by recognizing and exploring this new perspective on individuality, and hence diversity, in robotic swarms, we can gain a deeper understanding of these systems and potentially unlock new possibilities for their design and implementation of applications.

In order to strengthen animal welfare, many countries require that experimenters follow the ‘3Rs Principle’ when designing animal experiments. The 3Rs call for a reduction in the number of animals used, the refinement of methods to reduce stress as well as the full replacement of animals in experimentation through alternative methods. Biomimetic robots that resemble live animals and allow for natural-like interactions represent a valuable tool to achieve the 3Rs’ objectives. On the basis of our research with a robotic fish that is accepted as a conspecific by live poeciliid fishes, we highlight how biomimetic robots can reduce the number of animals tested by (a) substituting live animals, (b) providing highly standardized cues, and (c) reducing overall stress for live animals during tests through less handling.