An approach to evolve and exploit repertoires of general robot behaviours

Recent works in evolutionary robotics have shown the viability of evolution driven by behavioural novelty and diversity. These evolutionary approaches have been successfully used to generate repertoires of diverse and high-quality behaviours, instead of driving evolution towards a single, task-specific solution. Having repertoires of behaviours can enable new forms of robotic control, in which high-level controllers continually decide which behaviour to execute. To date, however, only the use of repertoires of open-loop locomotion primitives has been studied. We propose EvoRBC-II, an approach that enables the evolution of repertoires composed of general closed-loop behaviours, that can respond to the robot's sensory inputs. The evolved repertoire is then used as a basis to evolve a transparent higher-level controller that decides when and which behaviours of the repertoire to execute. Relying on experiments in a simulated domain, we show that the evolved repertoires are composed of highly diverse and useful behaviours. The same repertoire contains sufficiently diverse behaviours to solve a wide range of tasks, and the EvoRBC-II approach can yield a performance that is comparable to the standard tabula-rasa evolution. EvoRBC-II enables automatic generation of hierarchical control through a two-step evolutionary process, thus opening doors for the further exploration of the advantages that can be brought by hierarchical control.info:eu-repo/semantics/acceptedVersio

DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics

Robots are still limited to controlled conditions, that the robot designer knows with enough details to endow the robot with the appropriate models or behaviors. Learning algorithms add some flexibility with the ability to discover the appropriate behavior given either some demonstrations or a reward to guide its exploration with a reinforcement learning algorithm. Reinforcement learning algorithms rely on the definition of state and action spaces that define reachable behaviors. Their adaptation capability critically depends on the representations of these spaces: small and discrete spaces result in fast learning while large and continuous spaces are challenging and either require a long training period or prevent the robot from converging to an appropriate behavior. Beside the operational cycle of policy execution and the learning cycle, which works at a slower time scale to acquire new policies, we introduce the redescription cycle, a third cycle working at an even slower time scale to generate or adapt the required representations to the robot, its environment and the task. We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots. We describe results obtained so far with this approach and end up with a discussion of the questions it raises in Neuroscience

Recent trends in robot learning and evolution for swarm robotics

Swarm robotics is a promising approach to control large groups of robots. However, designing the individual behavior of the robots so that a desired collective behavior emerges is still a major challenge. In recent years, many advances in the automatic design of control software for robot swarms have been made, thus making automatic design a promising tool to address this challenge. In this article, I highlight and discuss recent advances and trends in offline robot evolution, embodied evolution, and offline robot learning for swarm robotics. For each approach, I describe recent design methods of interest, and commonly encountered challenges. In addition to the review, I provide a perspective on recent trends and discuss how they might influence future research to help address the remaining challenges of designing robot swarms

Immune-Inspired Fault Diagnosis for Robot Swarms

Previous work has shown that robot swarms are not always tolerant to the failure of individual robots, particularly those that have only partially failed and continue to contribute to collective behaviours. A case has been made for an active approach to fault tolerance in swarm robotic systems, whereby the swarm can identify and resolve faults that occur during operation. Existing approaches to active fault tolerance in swarms have so far omitted fault diagnosis, however this thesis proposes that fault diagnosis is a feature of active fault tolerance that is necessary if robot swarms are to achieve long-term autonomy. This thesis presents a novel method for fault diagnosis in robot swarms that attempts to imitate some of the observed functions of natural immune system. The experimental results presented in this thesis, which were obtained in software simulation and with actual robot hardware, show that the proposed fault diagnosis system is flexible, scalable, and improves swarm tolerance to various electro-mechanical faults in the cases examined

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

The disgusted mind: investigating the effects of parasite stress on social behaviour and beliefs

The parasite-stress theory of values and sociality offers a compelling evolutionary explanation as to why and how there is such wide variation and diversity of cultures and their underlying value and belief systems. Its authors propose that temporal and geographical variation in parasite stress in the ecological environment imposes causal effects on human behaviour by activating the behavioural immune system and motivating assortative sociality, i.e. philopatry, ethnocentrism, xenophobia, and religiosity. High parasite stress levels motivate strong assortative sociality thereby causing group isolation from which values and beliefs then arise and evolve independently and differently from outside groups, resulting in distinct cultural systems. There is an expanding body of correlational evidence to support this theory but critics argue that we should be cautious about attributing causal mechanisms. The main aim of this thesis was to provide some initial experimental tests of the parasite-stress theory. Four studies were conducted in this endeavour. The first study generated a new cross-culturally validated four-factor disgust image set to be employed in the subsequent studies as visual parasite stress. The next study tested whether variation in parasite stress could generate variation in the value given to physical attractiveness as a phenotypic indicator of genetic quality. The third study tested whether variation in parasite stress could lead individuals to diverge in their preferences for assortative versus prosocial rule systems in the formation of a hypothetical new society. Whereas, the final study tested whether variation in parasite stress could generate variation in the expression of assortative social behaviours. Results were mixed. The third study provided support for the parasite-stress theory, while the second and fourth studies did not. However, as these studies did support the evolutionary theory on which the parasite-stress theory is founded, the findings may be products of design issues. The parasite-stress theory is still valid and ripe for experimental investigation