A Novel Concept for the Study of Heterogeneous Robotic Swarms warm robotics systems are characterized by decentralized control, limited communication between robots, use of local information, and emergence of global behavior. Such systems have shown their potential for flexibility and robustness [1]-[3]. However, existing swarm robotics systems are by and large still limited to displaying simple proof-of-concept behaviors under laboratory conditions. It is our contention that one of the factors holding back swarm robotics research is the almost universal insistence on homogeneous system components. We believe that swarm robotics designers must embrace heterogeneity if they ever want swarm robotics systems to approach the complexity required of real-world systems. To date, swarm robotics systems have almost exclusively comprised physically and behaviorally undifferentiated agents. This design decision has its roots in ethological models of self-organizing natural systems. These models serve as inspiration for swarm robotics system designers, but are often highly abstract simplifications of natural systems and, to date, have largely assumed homogeneous agents. Selected dynamics of the systems under study are shown to emerge from the interactions of identical system components, ignoring the heterogeneities (physical, spatial, functional, and informational) that one can find in almost any natural system. The field of swarm robotics currently lacks methods and tools with which to study and leverage the heterogeneity that is present in natural systems. To remedy this deficiency, we propose swarmanoid, an innovative swarm robotics system composed of three different robot types with complementary skills: foot-bots are small autonomous robots specialized in moving on both even and uneven terrains, capable of self-assembling and of transporting objects or other robots; hand-bots are autonomous robots capable of climbing some vertical surfaces and manipulating small objects; and eye-bots are autonomous flying robots that can attach to an indoor ceiling, capable of analyzing the environment from a privileged position to S

Abstract:We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment.Evolutionary Robotics: A new scientific tool for studying cognition The recent history of Evolutionary RoboticsEvolutionary Robotics (ER) is a term that has gained currency since the early 1990s for the study and application of an artificial analogue of natural Darwinian evolution to the design of robots or simulated agents; usually to the design of their control systems or 'artificial brains', but sometimes also to their bodily and sensorimotor design [1,2]. This was not a new idea -nearly 50 years earlier Alan Turing talked of designing brain-like networks through "genetical search" [3] -but a combination of factors perhaps made the conditions friendly to the re-emergence of such an approach.After decades of dominance by the computational paradigm of Good Old Fashioned Artificial Intelligence (GOFAI), in the 1980s there was a resurgence of interest in Artificial Neural Networks (ANNs), Admittedly, as the phrase "Parallel Distributed Processing" indicates [4], this was thought of by most of its proponents as some new form of "biologically plausible" computational processing, and for the most part went along with similar Cartesian assumptions to GOFAI. But this did at least open some people's eyes to the possibility that brains, both real and artificial, were possibly not doing anything like computation at all -computation in the sense that Turing defined. At the same time in the 1980s the development of personal computing power made it possible for many more people to be ambitious in their simulations and experimentation.Turning from simulated brains to real robots, also in the 1980s Brooks developed a behaviour-based approach to robotics using subsumption architecture [5]. He designed minimal "insect-like" robots in an incremental fashion explicitly modelled on the process of natural evolution. A simple robot was constructed with sensors, motors and just about the smallest conceivable amount of "artificial nervous system" so as to perform in real time the simplest possible of behaviours; for instance, forward movement avoiding obstacles. Only after this simplest level of behaviour was tested and debugged on the real robot was the next stage attempted: adding a next simple layer of behaviour that interacted with the environment and the pre-existing behaviour so as to slightl...

Abstract-This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.

In recent years, there has been a growing interest in designing multi-robot systems (hereafter MRSs) to provide cost effective, fault-tolerant and reliable solutions to a variety of automated applications. Here, we review recent advancements in MRSs specifically designed for cooperative object transport, which requires the members of MRSs to coordinate their actions to transport objects from a starting position to a final destination. To achieve cooperative object transport, a wide range of transport, coordination and control strategies have been proposed. Our goal is to provide a comprehensive summary for this relatively heterogeneous and fast-growing body of scientific literature. While distilling the information, we purposefully avoid using hierarchical dichotomies, which have been traditionally used in the field of MRSs. Instead, we employ a coarse-grain approach by classifying each study based on the transport strategy used; pushing-only, grasping and caging. We identify key design constraints that may be shared among these studies despite considerable differences in their design methods. In the end, we discuss several open challenges and possible directions for future work to improve the performance of the current MRSs. Overall, we hope to increasethe visibility and accessibility of the excellent studies in the field and provide a framework that helps the reader to navigate through them more effectively.