An open question for both natural and artificial evolutionary systems is how, and under what environmental and evolutionary conditions complexity evolves. This study investigates the impact of increasingly complex tas...
详细信息
ISBN:
(纸本)9781450383509
An open question for both natural and artificial evolutionary systems is how, and under what environmental and evolutionary conditions complexity evolves. This study investigates the impact of increasingly complex task environments on the evolution of robot complexity. Specifically, the impact of evolving body-brain couplings on locomotive task performance, where robot evolution was directed by either body-brain exploration (novelty search) or objective-based (fitness function) evolutionary search. Results indicated that novelty search enabled the evolution of increased robot body-brain complexity and efficacy given specific environment conditions. The key contribution is thus the demonstration that body-brain exploration is suitable for evolving robot complexity that enables high fitness robots in specific environments.
Jamming Grippers are a novel class of soft robotic actuators that can robustly grasp and manipulate objects of arbitrary shape. They are formed by placing a granular material within a flexible skin connected to a vacu...
详细信息
ISBN:
(纸本)9781450383509
Jamming Grippers are a novel class of soft robotic actuators that can robustly grasp and manipulate objects of arbitrary shape. They are formed by placing a granular material within a flexible skin connected to a vacuum pump and function by pressing the unjammed gripper against a target object and evacuating the air to transition the material to a jammed (solid) state, gripping the target object. However, due to the complex interactions between grain morphology and target object shape, much uncertainty still remains regarding optimal constituent grain shapes for specific gripping applications. We address this challenge by utilising a modern evolutionary Algorithm, NSGA-III, combined with a Discrete Element Method soft robot model to perform a multi-objective optimisation of grain morphology for use in jamming grippers for a range of target object sizes. Our approach optimises the microscopic properties of the system to elicit bespoke functional granular material performance driven by the complex relationship between the individual particle morphologies and the related emergent behaviour of the bulk state. Results establish the important contribution of grain morphology to gripper performance and the critical role of local surface curvature and the length scale governed by the relative sizes of the grains and target object.
Robots are arguably essential for space research in the future, but designing and producing robots for unknown environments represents a grand challenge. The field of evolutionary robotics offers a solution by applyin...
详细信息
ISBN:
(纸本)9781728190488
Robots are arguably essential for space research in the future, but designing and producing robots for unknown environments represents a grand challenge. The field of evolutionary robotics offers a solution by applying the principles of natural evolution to robot design. In this paper, we consider a Moon-like environment and investigate the joint evolution of morphologies (bodies) and controllers (brains) when fitness is determined by the ability to locomote. In particular, we are interested in the evolved morphologies and compare the emerging 'life forms' in a Moonlike environment to those evolved under Earth-like conditions. To model the Moon we change two environmental properties of our baseline environment that represents the Earth: gravity is set to a low value and the flat terrain is replaced by the NASA model of the Moon landing site of the Apollo 14. The results show that changing only one of these does not lead to different evolved robot morphologies, but changing both does. Our evolved Moonwalkers are usually bigger, have fewer limbs and a less space filling shape than the robots evolved on Earth.
As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its application to increasingly complex r...
详细信息
ISBN:
(纸本)9781450383509
As open-ended learning based on divergent search algorithms such as Novelty Search (NS) draws more and more attention from the research community, it is natural to expect that its application to increasingly complex real-world problems will require the exploration to operate in higher dimensional Behavior Spaces (BSs) which will not necessarily be Euclidean. Novelty Search traditionally relies on k-nearest neighbours search and an archive of previously visited behavior descriptors which are assumed to live in a Euclidean space. This is problematic because of a number of issues. On one hand, Euclidean distance and Nearest-neighbour search are known to behave differently and become less meaningful in high dimensional spaces. On the other hand, the archive has to be bounded since, memory considerations aside, the computational complexity of finding nearest neighbours in that archive grows linearithmically with its size. A sub-optimal bound can result in "cycling" in the behavior space, which inhibits the progress of the exploration. Furthermore, the performance of NS depends on a number of algorithmic choices and hyperparameters, such as the strategies to add or remove elements to the archive and the number of neighbours to use in k-nn search. In this paper, we discuss an alternative approach to novelty estimation, dubbed Behavior Recognition based Novelty Search (BR-NS), which does not require an archive, makes no assumption on the metrics that can be defined in the behavior space and does not rely on nearest neighbours search. We conduct experiments to gain insight into its feasibility and dynamics as well as potential advantages over archive-based NS in terms of time complexity.
Designing robots by hand can be costly and time consuming, especially if the robots have to be created with novel materials, or be robust to internal or external changes. In order to create robots automatically, witho...
详细信息
ISBN:
(纸本)9783030726980;9783030726997
Designing robots by hand can be costly and time consuming, especially if the robots have to be created with novel materials, or be robust to internal or external changes. In order to create robots automatically, without the need for human intervention, it is necessary to optimise both the behaviour and the body design of the robot. However, when co-optimising the morphology and controller of a locomoting agent the morphology tends to converge prematurely, reaching a local optimum. Approaches such as explicit protection of morphological innovation have been used to reduce this problem, but it might also be possible to increase exploration of morphologies using a more indirect approach. We explore how changing the environment, where the agent locomotes, affects the convergence of morphologies. The agents' morphologies and controllers are co-optimised, while the environments the agents locomote in are evolved open-endedly with the Paired Open-Ended Trailblazer (POET). We compare the diversity, fitness and robustness of agents evolving in environments generated by POET to agents evolved in handcrafted curricula of environments. Our agents each contain of a population of individuals being evolved with a genetic algorithm. This population is called the agent-population. We show that agent-populations evolving in open-endedly evolving environments exhibit larger morphological diversity than agent-populations evolving in hand crafted curricula of environments. POET proved capable of creating a curriculum of environments which encouraged both diversity and quality in the populations. This suggests that POET may be capable of reducing premature convergence in co-optimisation of morphology and controllers.
evolutionary robotics employs unconventional techniques to continuously evolve controllers for robots, based on their fitness values. In most cases, a parent controller is subjected to mutation to evolve its offspring...
详细信息
evolutionary robotics employs unconventional techniques to continuously evolve controllers for robots, based on their fitness values. In most cases, a parent controller is subjected to mutation to evolve its offspring. If the offspring performs better than the parent, the former is made to replace the latter. This essentially results in a major loss in information learned by the parents over generations. One simple workaround to circumvent this problem is to maintain a Hall of Fame (HoF) comprising the best parent controllers for use in future generations. In embodied evolutionary robotic scenarios, caching a large number of controllers in an HoF would result in increased computational overheads while selecting the best out of them, resulting in a drastic reduction in performance. With no means to find an upper limit to the number of controllers that can populate an HoF a priori , devising a technique to dynamically regulate this population is imperative. In this work, a novel method to evict the non-performing controllers within an HoF based on the dynamics of the system, is proposed. We describe the evolution of such controllers using genetic operators that eventually form an Idiotypic Network. A constantly varying Resource , associated with each controller together with its concentration in the Idiotypic Network, helps decide its eviction from the HoF. Experiments performed using simulations and also a real robot indicate a marked improvement in the learning process due to the dynamic eviction policy.
evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because ener...
详细信息
evolutionary robotics is concerned with optimizing autonomous robots for one or more specific tasks. Remarkably, the energy needed to operate autonomously is hardly ever considered. This is quite striking because energy consumption is a crucial factor in real-world applications and ignoring this aspect can increase the reality gap. In this paper, we aim to mitigate this problem by extending our robot simulator framework with a model of a battery module and studying its effect on robot evolution. The key idea is to include energy efficiency in the definition of fitness. The robots will need to evolve to achieve high gait speed and low energy consumption. Since our system evolves the robots' morphologies as well as their controllers, we investigate the effect of the energy extension on the morphologies and on the behavior of the evolved robots. The results show that by including the energy consumption, the evolution is not only able to achieve higher task performance (robot speed), but it reaches good performance faster. Inspecting the evolved robots and their behaviors discloses that these improvements are not only caused by better morphologies, but also by better settings of the robots' controller parameters.
Previous research has shown automated robotic mechanism design to be both deceptive (prone to local minima) and rife with linkage problems (having highly interdependent parameters). This results in a barrier to optimi...
详细信息
Previous research has shown automated robotic mechanism design to be both deceptive (prone to local minima) and rife with linkage problems (having highly interdependent parameters). This results in a barrier to optimization that is unable to be breached by simply applying more iterations and computational power. The research also indicates that a graph structure model of the robot in combination with an evolutionary algorithm yields useful robotic mechanisms for a limited set of simple problems. This thesis expands on this pre-existing representation by introducing an indirect model that can be used to include both controllers, motors and other new elements in the representation. Besides this extension of the mechanism model, a framework for the automated design optimization task itself is introduced. This thesis shows an equivalence between an operator based representation of the mechanisms and the graph based representation. These operators represent modifications on the mechanism structure and/or parameters. By recognizing the operators as paths in this model a graph of the search space itself can be constructed. In this graph the vertices are mechanisms and the edges are operators. Using the the operator-mechanism equivalence it is shown that designing an optimization algorithm is equivalent to (1) choosing how vertices in the space are grouped together. (2) choosing how the vertices of this search space are connected beforehand by either implicitly or explicitly picking operators and projecting onto their corresponding domain. (3) picking which of the connected paths to traverse based on accumulated information at runtime. This represents a framework that allows the accumulation of knowledge about optimization algorithms acting within it by defining a set of meta-heuristics. With these it is possible to make informed choices to build better optimization algorithms. To show the effectiveness of the framework a novel quality diversity algorithm is developed, Redu
I present criteria or benchmarks of personhood that robotic agents must pass for them to match us in social intelligence and indicate some current successes and difficulties for the future with respect to these criter...
详细信息
I present criteria or benchmarks of personhood that robotic agents must pass for them to match us in social intelligence and indicate some current successes and difficulties for the future with respect to these criteria. I focus on five problematic areas: 1) How can inorganic material substances mimic organic processes?;2) How can robots evolve adaptations similar to those of human persons?;3) How can robots develop into adult personhood?;4) How can social identities of robots transform in an ever evolving robotic society?;5) How can robots display intellectual and moral ideals that can advance knowledge and social goals?
Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by ei...
详细信息
ISBN:
(纸本)9781450319638
Methodologies are emerging in many branches of computer science that demonstrate how human users and automated algorithms can collaborate on a problem such that their combined solutions outperform those produced by either humans or algorithms alone. The problem of behavior optimization in robotics seems particularly well-suited for this approach because humans have intuitions about how animals-and thus robots-should and should not behave, and can visually detect non-optimal behaviors that are trapped in local optima. Here we introduce a multiobjective approach in which a surrogate user (which stands in for a human user) deflects search away from local optima and a traditional fitness function eventually leads search toward the global optimum. We show that this approach produces superior solutions for a deceptive robotics problem compared to a similar search method that is guided by just a surrogate user or just a fitness function.
暂无评论