We present a distributed Bayesian algorithm for robot swarms to classify a spatially distributed feature of an environment. This type of "go/no-go" decision appears in applications where a group of robots must collectively choose whether to take action, such as determining if a farm field should be treated for pests. Previous bio-inspired approaches to decentralized decision-making in robotics lack a statistical foundation, while decentralized Bayesian algorithms typically require a strongly connected network of robots. In contrast, our algorithm allows simple, sparsely distributed robots to quickly reach accurate decisions about a binary feature of their environment. We investigate the speed vs. accuracy tradeoff in decision-making by varying the algorithm's parameters. We show that making fewer, less-correlated observations can improve decision-making accuracy, and that a well-chosen combination of prior and decision threshold allows for fast decisions with a small accuracy cost. Both speed and accuracy also improved with the addition of bio-inspired positive feedback. This algorithm is also adaptable to the difficulty of the environment. Compared to a fixed-time benchmark algorithm with accuracy guarantees, our Bayesian approach resulted in equally accurate decisions, while adapting its decision time to the difficulty of the environment.

We study the problem of controlling a swarm of anonymous, mobile robots to cooperatively cover an unknown two-dimensional space. The novelty of our proposed solution is that it is applicable to extremely simple robots that lack run-time computation or storage. The solution requires only a single bit of information per robot-whether or not another robot is present in its line of sight. Computer simulations show that our deterministic controller, which was obtained through off-line optimization, achieves around 71-76% coverage in a test scenario with no robot redundancy, which corresponds to a 26-39% reduction of the area that is not covered, when compared to an optimized random walk. A moderately lower level of performance was observed in 20 experimental trials with 25 physical e-puck robots. Moreover, we demonstrate that the same controller can be used in environments of different dimensions and even to navigate a maze. The controller provides a baseline against which one can quantify the performance improvements that more advanced and expensive techniques may offer. Moreover, due to its simplicity, it could potentially be implemented on swarms of sub-millimeter-sized robots. This would pave the way for new applications in micro-medicine.

Recently, a new class of machine learning algorithms has emerged, where models and discriminators are generated in a competitive setting. The most prominent example is Generative Adversarial Networks (GANs). In this paper we examine how these algorithms relate to the Turing test, and derive what - from a Turing perspective - can be considered their defining features. Based on these features, we outline directions for generalizing GANs - resulting in the family of algorithms referred to as Turing Learning. One such direction is to allow the discriminators to interact with the processes from which the data samples are obtained, making them "interrogators", as in the Turing test. We validate this idea using two case studies. In the first case study, a computer infers the behavior of an agent while controlling its environment. In the second case study, a robot infers its own sensor configuration while controlling its movements. The results confirm that by allowing discriminators to interrogate, the accuracy of models is improved.

This paper proposes a strategy for transporting a large object to a goal using a large number of mobile robots that are significantly smaller than the object. The robots only push the object at positions where the direct line of sight to the goal is occluded by the object. This strategy is fully decentralized and requires neither explicit communication nor specific manipulation mechanisms. We prove that it can transport any convex object in a planar environment. We implement this strategy on the e-puck robotic platform and present systematic experiments with a group of 20 e-pucks transporting three objects of different shapes. The objects were successfully transported to the goal in 43 out of 45 trials. When using a mobile goal, teleoperated by a human, the object could be navigated through an environment with obstacles. We also tested the strategy in a 3-D environment using physics-based computer simulation. Due to its simplicity, the transport strategy is particularly suited for implementation on microscale robotic systems.

We propose a coevolutionary approach for learning the behavior of animals, or agents, in collective groups. The approach requires a replica that resembles the animal under investigation in terms of appearance and behavioral capabilities. It is able to identify the rules that govern the animals in an autonomous manner. A population of candidate models, to be executed on the replica, compete against a population of classifiers. The replica is mixed into the group of animals and all individuals are observed. The fitness of the classifiers depends solely on their ability to discriminate between the replica and the animals based on their motion over time. Conversely, the fitness of the models depends solely on their ability to 'trick' the classifiers into categorizing them as an animal. Our approach is metric-free in that it autonomously learns how to judge the resemblance of the models to the animals. It is shown in computer simulation that the system successfully learns the collective behaviors of aggregation and of object clustering. A quantitative analysis reveals that the evolved rules approximate those of the animals with a good precision.

This paper proposes a method that allows a machine to infer the behavior of an animal in a fully automatic way. In principle, the machine does not need any prior information about the behavior. It is able to modify the environmental conditions and observe the animal; therefore it can learn about the animal through controlled interaction. Using a competitive coevolutionary approach, the machine concurrently evolves animats, that is, models to approximate the animal, as well as classifiers to discriminate between animal and animat. We present a proof-of-concept study conducted in computer simulation that shows the feasibility of the approach. Moreover, we show that the machine learns significantly better through interaction with the animal than through passive observation. We discuss the merits and limitations of the approach and outline potential future directions.

This paper proposes a strategy for transporting a tall, and potentially heavy, object to a goal using a large number of miniature mobile robots. The robots move the object by pushing it. The direction in which the object moves is controlled by the way in which the robots distribute themselves around its perimeter - if the robots dynamically reallocate themselves around the section of the object's perimeter that occludes their view of the goal, the object will eventually be transported to the goal. This strategy is fully distributed, and makes no use of communication between the robots. A controller based on this strategy was implemented on a swarm of 12 physical e-puck robots, and a systematic experiment with 30 randomized trials was performed. The object was successfully transported to the goal in all the trials. On average, the path traced by the object was about 8.4% longer than the shortest possible path.