Swarm intelligence refers to the phenomenon of a system of spatially distributed individuals that coordinate their actions in a decentralized and self-organized manner, so as to exhibit complex collective behavior. Such systems tend to have large numbers of individual agents that interact with each other in simple ways. This allows swarm-intelligent systems to be inherently robust and flexible. As these principles are scale-free, systems with these properties can range in size from the nano to the macro scale. Swarm-intelligent systems are common throughout nature. Examples are bacteria colonies, neural networks, social insects, and flocks/herds of vertebrates. In addition, humans have produced a variety of (artificial) swarm systems ranging from swarm-based optimization algorithms to sensor networks, swarms of robots, and smart materials. In each of these natural or artificial systems, populations of agents change their spatiotemporal configuration solely based on the agents’ local interactions with each other and the environment. This special issue on ‘‘Swarm Robotics’’ provides an overview of recent results and trends in this emerging field. Contributions to this special issue range from programming paradigms for swarming systems to specific distributed algorithms and modular robotic systems. The unifying theme of these works is individual simplicity: complex global behavior emerges from purely local interactions and simple local rules. Examples covered in this special issue range from spatial behaviors such as flocking and dispersion, computational behaviors such as shortest-path routing and collective decisions, up to full-body behaviors of modular robot ensembles. In their paper, ‘‘Composable continuous-space programs for robotic swarms’’, Bachrach, Beal, and McLurkin present the functional programming language Proto that allows individual behavior to be described by expressions over a global field. By computing the global field not only from local measurements but also based on data received from other swarm members within the local neighborhood, previous state, and control logic, Proto allows complex swarming behaviors to be composed with highly compact code. Proto code is then compiled into op-codes for the Proto Virtual Machine, which needs to provide abstractions for sensing, actuation, estimation of the geometric relations between neighboring swarm members, and local communication. Algorithms such as shortest-path routing are demonstrated on a swarm of 40 miniature mobile robots, as well as in computer simulations. In their paper, ‘‘Collective decision-making based on social odometry’’, Gutierrez, Campo, Monasterio-Huelin, Magdalena, and Dorigo investigate a novel collective decision-making mechanism using a colony of mobile robots that accomplish a foraging task. The robots are required to establish a path from a central place to the closest of multiple resource sites. To reach a consensus, they make use of social odometry. The latter mechanism enables the robots to estimate the position of resource sites by exchanging and aggregating odometry-based positional information and confidence levels. The collective decisionmaking mechanism is successfully validated by experiment N. Correll (&) Department of Computer Science, University of Colorado at Boulder, 430 UCB, Boulder, CO 80309, USA e-mail: nikolaus.correll@colorado.edu