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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Doney, Kyle Petridou, Aikaterini Karaul, Jacob Khan, Ali Liu, Geoffrey Rieffel, John Union Coll Comp Sci Dept Schenectady NY 12308 USA

ISBN: (纸本)9781728125473

Mobile soft robots offer compelling applications in fields ranging from urban search and rescue to planetary exploration. A critical challenge of soft robotic control is that the nonlinear dynamics imposed by soft materials often result in complex behaviors that are counter-intuitive and hard to model or predict. As a consequence, most behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. A second challenge is that soft materials are difficult to simulate with high fidelity - leading to a significant reality gap when trying to discover or optimize new behaviors. In this work we employ a Quality Diversity Algorithm running model-free on a physical soft tensegrity robot that autonomously generates a behavioral repertoire with no a priori knowledge of the robot's dynamics, and minimal human intervention. The resulting behavior repertoire displays a diversity of unique locomotive gaits useful for a variety of tasks. These results help provide a road map for increasing the behavioral capabilities of mobile soft robots through real-world automation.

关键词： Soft robotics Quality Diversity Algorithms Tensegrity evolutionary robotics

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Incorporating User Feedback in Embodied Evolution 17

Incorporating User Feedback in Embodied Evolution

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Genetic and evolutionary Computation Conference (GECCO)

作者： Kemeling, Micha Haasdijk, Evert Vrije Univ Amsterdam Dept Comp Sci De Boelelaan 1081 A NL-1081 HV Amsterdam Netherlands

ISBN: (纸本)9781450349390

We investigate the possibilities of incorporating user feedback at run-time in an embodied evolution setting. User feedback in this case consists of a user ranking a small sample from the population in an ongoing evolutionary process according to some criterion. We describe an approach to develop a surrogate model, disseminate that model across the evolving robot collective and incorporate it as an additional objective during the evolutionary run. We evaluate the method in a number of scenarios, showing that this approach does to some extent cause the collective to absorb new objectives, including objectives to refrain from particular behaviour, but not to the level that is achieved by explicitly adding the true objective function.

关键词： evolutionary robotics Embodied Evolution Interactive Evolution

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BR-NS: an Archive-less Approach to Novelty Search

BR-NS: an Archive-less Approach to Novelty Search

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2nd Genetic and evolutionary Computation Conference (GECCO)

作者： Salehi, Achkan Coninx, Alexandre Doncieux, Stephane Sorbonne Univ CNRS ISIR F-75005 Paris France

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.

关键词： Novelty Search Behavior Space evolutionary robotics

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Co-imagination of Behaviour and Morphology of Agents 9th

Co-imagination of Behaviour and Morphology of Agents

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9th Annual Conference on Machine Learning, Optimization and Data science (LOD)

作者： Sliacka, Maria Mistry, Michael Calandra, Roberto Kyrki, Ville Luck, Kevin Sebastian Aalto Univ Dept Elect Engn & Automat EEA Espoo Finland Univ Edinburgh Edinburgh Midlothian Scotland Tech Univ Dresden Learning Adapt Syst & Robot LASR Lab Dresden Germany Ctr Tactile Internet Human Loop CeTI Dresden Germany Finnish Ctr Artificial Intelligence Espoo Finland Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9783031539688;9783031539695

The field of robot learning has made great advances in developing behaviour learning methodologies capable of learning policies for tasks ranging from manipulation to locomotion. However, the problem of combined learning of behaviour and robot structure, here called co-adaptation, is less studied. Most of the current co-adapting robot learning approaches rely on model-free algorithms or assume to have access to an a-priori known dynamics model, which requires considerable human engineering. In this work, we investigate the potential of combining model-free and model-based reinforcement learning algorithms for their application on co-adaptation problems with unknown dynamics functions. Classical model-based reinforcement learning is concerned with learning the forward dynamics of a specific agent or robot in its environment. However, in the case of jointly learning the behaviour and morphology of agents, each individual agent-design implies its own specific dynamics function. Here, the challenge is to learn a dynamics model capable of generalising between the different individual dynamics functions or designs. In other words, the learned dynamics model approximates a multi-dynamics function with the goal to generalise between different agent designs. We present a reinforcement learning algorithm that uses a learned multi-dynamics model for co-adapting robot's behaviour and morphology using imagined rollouts. We show that using a multi-dynamics model for imagining transitions can lead to better performance for model-free co-adaptation, but open challenges remain.

关键词： evolutionary robotics Co-Adaptation Co-Design Reinforcement Learning

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Growth-Based Morphological Development: A Natural Approach to Fitness Landscape Shaping 22

Growth-Based Morphological Development: A Natural Approach t...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Naya-Varela, Martin Faina, Andres Duro, Richard J. Univ A Coruna Integrated Grp Engn Res La Coruna Spain Univ A Coruna CITIC La Coruna Spain IT Univ Copenhagen Comp Sci Dept Copenhagen Denmark IT Univ Copenhagen REAL Lab Copenhagen Denmark

ISBN: (纸本)9781450392686

In nature, the morphological changes that occur as cognitive development takes place in human beings and animals have been shown to facilitate learning. Taking inspiration from nature, morphological development aimed at improving learning has been applied in the literature to different robotics configurations and tasks with mixed results. In this paper, we consider a growth-based morphological development approach applied to a quadruped walking task, and, in addition to determining that it improves the learning characteristics, we show that morphological development is something other than just introducing noise as a fitness landscape smoothing technique. Finally, by means of a study of the evolution of the different fitness landscapes, we argue and discuss that learning using growth is equivalent to an incremental learning or fitness landscape shaping approach.

关键词： evolutionary robotics Morphological Development Neuroevolution Noisy Optimization Fitness Landscape Shaping

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Crowdseeding: A Novel Approach for Designing Bioinspired Machines 4th

Crowdseeding: A Novel Approach for Designing Bioinspired Mac...

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4th International Conference on Biomimetic and Biohybrid Systems (Living Machines)

作者： Wagy, Mark D. Bongard, Josh C. Univ Vermont Dept Comp Sci Burlington VT 05405 USA

ISBN: (纸本)9783319229799;9783319229782

Crowdsourcing is a popular technique for distributing tasks to a group of anonymous workers over the web. Similarly, crowdseeding is any mechanism that extracts knowledge from the crowd, and then uses that knowledge to guide an automated process. Here we demonstrate a method that automatically distills features from a set of robot body plans designed by the crowd, and then uses those features to guide the automated design of robot body plans and controllers. This approach outperforms past work in which one feature was detected and distilled manually. This provides evidence that the crowd collectively possesses intuitions about the biomechanical advantages of certain body plans;we hypothesize that these intuitions derive from their experiences with biological organisms.

关键词： evolutionary robotics Biomimetics Crowdsourcing Data mining Machine learning

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Efficient Concurrent Design of the Morphology of Unmanned Aerial Systems and their Collective-Search Behavior

Efficient Concurrent Design of the Morphology of Unmanned Ae...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zeng, Chen KrisshnaKumar, Prajit Witter, Jhoel Chowdhury, Souma Univ Buffalo Mech & Aerosp Engn Buffalo NY 14260 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

The collective operation of robots, such as unmanned aerial vehicles (UAVs) operating as a team or swarm, is affected by their individual capabilities, which in turn is dependent on their physical design, aka morphology. However, with the exception of a few (albeit ad hoc) evolutionary robotics methods, there has been very little work on understanding the interplay of morphology and collective behavior. There is especially a lack of computational frameworks to concurrently search for the robot morphology and the hyper-parameters of their behavior model that jointly optimize the collective (team) performance. To address this gap, this paper proposes a new co-design framework. Here the exploding computational cost of an otherwise nested morphology/behavior co-design is effectively alleviated through the novel concept of "talent" metrics;while also allowing significantly better solutions compared to the typically sub-optimal sequential *** design approach. This framework comprises four major steps: talent metrics selection, talent Pareto exploration (a multi-objective morphology optimization process), behavior optimization, and morphology finalization. This co-design concept is demonstrated by applying it to design UAVs that operate as a team to localize signal sources, e.g., in victim search and hazard localization. Here, the collective behavior is driven by a recently reported batch Bayesian search algorithm called Bayes-Swarm. Our case studies show that the outcome of co-design provides significantly higher success rates in signal source localization compared to a baseline design, across a variety of signal environments and teams with 6 to 15 UAVs. Moreover, this co-design process provides two orders of magnitude reduction in computing time compared to a projected nested design approach.

关键词： Measurement Location awareness evolutionary robotics Computational modeling Morphology Hazards Behavioral sciences

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Evolving controllers for real robots: A survey of the literature

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ADAPTIVE BEHAVIOR 2003年第3期11卷 179-203页

作者： Walker, J Garrett, S Wilson, M Univ Wales Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales

For many years, researchers in the field of mobile robotics have been investigating the use of genetic and evolutionary computation (GEC) to aid the development of mobile robot controllers. Alongside the fundamental choices of the GEC mechanism and its operators, which apply to both simulated and physical evolutionary robotics, other issues have emerged which are specific to the application of GEC to physical mobile robotics. This article presents a survey of recent methods in GEC-developed mobile robot controllers, focusing on those methods that include a physical robot at some point in the learning loop. It simultaneously relates each of these methods to a framework of two orthogonal issues: the use of a simulated and/or a physical robot, and the use of finite, training phase evolution prior to a task and/or lifelong adaptation by evolution during a task. A list of evaluation criteria are presented and each of the surveyed methods are compared to them. Analyses of the framework and evaluation criteria suggest several possibilities;however, there appear to be particular advantages in combining simulated, training phase evolution (TPE) with lifelong adaptation by evolution (LAE) on a physical robot.

关键词： evolutionary robotics physical robots simulation training lifelong adaptation by evolution.

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A Quantitative Analysis of Memory Requirement and Generalization Performance for Robotic Tasks 07

A Quantitative Analysis of Memory Requirement and Generaliza...

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Annual Conference of Genetic and evolutionary Computation Conference

作者： Kim, DaeEun Yonsei Univ Sch Elect & Elect Engn Biol Cybernet Lab Seoul 120749 South Korea

ISBN: (纸本)9781595936974

In autonomous agent systems, memory is an important element to handle agent behaviors appropriately. We present the analysis of memory requirements for robotic tasks including wall following and corridor following. The robotic tasks are simulated with sensor modeling and motor actions in noisy environments. In this paper, control structures are based on finite state machines for memory-based controllers, and we use the evolutionary multiobjective optimization approach with two objectives, behavior performance and memory size. For each flask, a quantitative approach to estimate internal states with a different number of sensors is applied and the, best, controllers are evaluated in several test, environment to examine their generalization characteristics and efficiency. Finite state machines with a hierarchy of memory are also compared with feedforward neural networks for the behavior performance.

关键词： evolutionary robotics internal states perceptual aliasing evolutionary multiobjective optimization generalization behaviour

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An evolutionary method of adaptive behavior for robot based on echo state network

An evolutionary method of adaptive behavior for robot based ...

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8th World Congress on Intelligent Control and Automation (WCICA)

作者： Song Yong Li Yi-bin Liu Bing Shandong Univ Sch Control Sci & Engn Jinan 250100 Peoples R China Shandong Univ Sch Informat & Engn Weihai Peoples R China

ISBN: (纸本)9781424467129

For the re-evolution of the mobile robot behavior in unknown environments, the mapping relation was constructed between input of sensors and output of actuators based on echo state network. An algorithm of adaptive behavior learning was presented based on echo state network for evolutionary robotics. The composite architecture with resposive behavior and behavior learning was adopted. The resposive behavior was drived by the samples composed with sensor information and decision. The weights of echo state network were optimized via (mu+lambda)-evolution strategy. The new control rules were generated via evolutionary algorithms, and new samples were added to the database constantly. The high intelligent behaviors of robot were transmitted to resposive behaviors. The experimental results indicate that the proposed approach has a better adaptability.

关键词： evolutionary robotics adaptive behavior echo state networks evolutionary algorithms

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