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Adaptive Agents in Changing Environments, the Role of Modularity

NEURAL PROCESSING LETTERS 2015年第2期42卷 257-274页

作者： Calabretta, Raffaele Neirotti, Juan CNR Inst Cognit Sci & Technol Lab Autonomous Robot & Artificial Life Rome Italy Aston Univ NCRG Birmingham B4 7ET W Midlands England

We explored the role of modularity as a means to improve evolvability in populations of adaptive agents. We performed two sets of artificial life experiments. In the first, the adaptive agents were neural networks controlling the behavior of simulated garbage collecting robots, where modularity referred to the networks architectural organization and evolvability to the capacity of the population to adapt to environmental changes measured by the agents performance. In the second, the agents were programs that control the changes in network's synaptic weights (learning algorithms), the modules were emerged clusters of symbols with a well defined function and evolvability was measured through the level of symbol diversity across programs. We found that the presence of modularity (either imposed by construction or as an emergent property in a favorable environment) is strongly correlated to the presence of very fit agents adapting effectively to environmental changes. In the case of learning algorithms we also observed that character diversity and modularity are also strongly correlated quantities.

关键词： Evolvability Emergence of modularity Artificial life simulations evolutionary robotics

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Artificial Biochemical Networks: Evolving Dynamical Systems to Control Dynamical Systems

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2014年第2期18卷 145-166页

作者： Lones, Michael A. Fuente, Luis A. Turner, Alexander P. Caves, Leo S. D. Stepney, Susan Smith, Stephen L. Tyrrell, Andy M. Univ York York YO10 5DD N Yorkshire England

Biological organisms exist within environments in which complex nonlinear dynamics are ubiquitous. They are coupled to these environments via their own complex dynamical networks of enzyme-mediated reactions, known as biochemical networks. These networks, in turn, control the growth and behavior of an organism within its environment. In this paper, we consider computational models whose structure and function are motivated by the organization of biochemical networks. We refer to these as artificial biochemical networks and show how they can evolve to control trajectories within three behaviorally diverse complex dynamical systems: 1) the Lorenz system;2) Chirikov's standard map;and 3) legged robot locomotion. More generally, we consider the notion of evolving dynamical systems to control dynamical systems, and discuss the advantages and disadvantages of using higher order coupling and configurable dynamical modules (in the form of discrete maps) within artificial biochemical networks (ABNs). We find both approaches to be advantageous in certain situations, though we note that the relative tradeoffs between different models of ABN strongly depend on the type of dynamical systems being controlled.

关键词： Biochemical networks chaos control dynamical systems evolutionary robotics genetic programming

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Simulating Soft-Bodied Swimmers with Particle-Based Physics

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SOFT robotics 2019年第2期6卷 263-275页

作者： Johnson, Chris Philippides, Andrew Husbands, Philip Univ Sussex Ctr Computat Neurosci & Robot Brighton BN1 9RH E Sussex England

In swimming virtual creatures, there is often a disparity between the level of detail in simulating a swimmer's body and that of the fluid it moves in. To address this disparity, we have developed a new approach to modeling swimming virtual creatures using pseudo-soft bodies and particle-based fluids, which has sufficient realism to investigate a larger range of body-environment interactions than are usually included. As this comes with increased computational costs, which may be severe, we have also developed a means of reducing the volume of fluid that must be simulated.

关键词： fluid simulation evolutionary robotics embodiment morphology morphological computation adaptive behavior

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Effective Search for Control Hierarchies Within the Policy Decomposition Framework

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IEEE robotics AND AUTOMATION LETTERS 2024年第12期9卷 11114-11121页

作者： Khadke, Ashwin Geyer, Hartmut Carnegie Mellon Univ Robot Inst Sch Comp Sci Pittsburgh PA 15213 USA

Policy decomposition is a novel framework for approximating optimal control policies of complex dynamical systems with a hierarchy of policies derived from smaller but tractable subsystems. It stands out amongst the class of hierarchical control methods by estimating a priori how well the closed-loop behavior of different control hierarchies matches the optimal policy. However, the number of possible hierarchies grows prohibitively with the number of inputs and the dimension of the state-space of the system making it unrealistic to estimate the closed-loop performance for all hierarchies. Here, we present the development of two search methods based on Genetic Algorithm and Monte-Carlo Tree Search to tackle this combinatorial challenge, and demonstrate that it is indeed surmountable. We showcase the efficacy of our search methods and the generality of the framework by applying it towards finding hierarchies for control of three distinct robotic systems: a simplified biped, a planar manipulator, and a quadcopter. The discovered hierarchies, in comparison to heuristically designed ones, provide improved closed-loop performance or can be computed in minimal time with marginally worse control performance, and also exceed the control performance of policies obtained with popular deep reinforcement learning methods.

关键词： Optimal control Costs Monte Carlo methods Genetic algorithms Search methods Quadrotors Dynamical systems Deep reinforcement learning Control design Trajectory Optimization and optimal control evolutionary robotics reinforcement learning

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Cooperation Through Self-Assembly in Multi-Robot Systems

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ACM TRANSACTIONS ON AUTONOMOUS AND ADAPTIVE SYSTEMS 2006年第2期1卷 115-150页

作者： Tuci, Elio Gross, Roderich Trianni, Vito Mondada, Francesco Bonani, Michael Dorigo, Marco Univ Libre Bruxelles IRIDIA Brussels Belgium Ecole Polytech Fed Lausanne STI ASL LSAI2S CH-1015 Lausanne Switzerland

This article illustrates the methods and results of two sets of experiments in which a group of mobile robots, called s-bots, are required to physically connect to each other, that is, to self-assemble, to cope with environmental conditions that prevent them from carrying out their task individually. The first set of experiments is a pioneering study on the utility of self-assembling robots to address relatively complex scenarios, such as cooperative object transport. The results of our work suggest that the s-bots possess hardware characteristics which facilitate the design of control mechanisms for autonomous self-assembly. The control architecture we developed proved particularly successful in guiding the robots engaged in the cooperative transport task. However, the results also showed that some features of the robots' controllers had a disruptive effect on their performances. The second set of experiments is an attempt to enhance the adaptiveness of our multi-robot system. In particular, we aim to synthesise an integrated (i.e., not-modular) decision-making mechanism which allows the s-bot to autonomously decide whether or not environmental contingencies require self-assembly. The results show that it is possible to synthesize, by using evolutionary computation techniques, artificial neural networks that integrate both the mechanisms for sensory-motor coordination and for decision making required by the robots in the context of self-assembly.

关键词： Algorithms Swarm robotics evolutionary robotics self-assembly artificial neural networks evolutionary algorithms swarm intelligence

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Learning to Climb: Constrained Contextual Bayesian Optimisation on a Multi-Modal Legged Robot

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IEEE robotics AND AUTOMATION LETTERS 2022年第4期7卷 9881-9888页

作者： Yu, Chen Cao, Jinyue Rosendo, Andre ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China

Controlling a legged robot to climb obstacles with different heights is challenging, but important for an autonomous robot to work in an unstructured environment. In this paper, we model this problem as a novel contextual constrained multi-armed bandit framework. We further propose a learning-based Constrained Contextual Bayesian Optimisation (CoCoBo) algorithm that can solve this class of problems efficiently. CoCoBo models both the reward function and constraints as Gaussian processes, incorporate continuous context space and action space into each Gaussian process, and find the next training samples through excursion search. The experimental results show that CoCoBo is more data-efficient and safe, compared to other related state-of-the-art optimisation methods, on both synthetic test functions and real-world experiments. Our real-world results-our robot could successfully learn to climb an obstacle higher than itself-reveal that our method has an enormous potential to allow self-adaptive robots to work in various terrains.

关键词： Bayesian optimisation bioinspired robot learning climbing robots evolutionary robotics

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Evolved synaptic delay based neural controllers for walking patterns in hexapod robotic structures

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NATURAL COMPUTING 2017年第2期16卷 201-211页

作者： Santos, Jose Fernandez, Pablo Univ A Coruna Comp Sci Dept La Coruna Spain

In this work the center-crossing condition was integrated in artificial neural networks that incorporate synaptic delays in their connections. These synaptic delay based neural networks act as Central Pattern Generators (CPGs) for walking controllers in hexapod robotic structures. Simulated evolution is used to automatically obtain such neural controllers for walking behaviors. The optimized controllers show the time reasoning capabilities of the synaptic delay based neural networks for the temporal coordination of the hexapod joints. We compared the results against continuous time recurrent neural networks, one of the neural models most used as CPG, when proprioceptive information is used to provide fault tolerance for the required behavior.

关键词： Neural controllers evolutionary robotics Central pattern generators

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An Experiment on Behavior Generalization and the Emergence of Linguistic Compositionality in Evolving Robots

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IEEE TRANSACTIONS ON AUTONOMOUS MENTAL DEVELOPMENT 2011年第2期3卷 176-189页

作者： Tuci, Elio Ferrauto, Tomassino Zeschel, Arne Massera, Gianluca Nolfi, Stefano CNR ISTC I-00185 Rome Italy Univ So Denmark Inst Business Commun & Informat Sci DK-6400 Sonderborg Denmark

Populations of simulated agents controlled by dynamical neural networks are trained by artificial evolution to access linguistic instructions and to execute them by indicating, touching, or moving specific target objects. During training the agent experiences only a subset of all object/action pairs. During postevaluation, some of the successful agents proved to be able to access and execute also linguistic instructions not experienced during training. This owes to the development of a semantic space, grounded in the sensory motor capability of the agent and organized in a systematized way in order to facilitate linguistic compositionality and behavioral generalization. Compositionality seems to be underpinned by a capability of the agents to access and execute the instructions by temporally decomposing their linguistic and behavioral aspects into their constituent parts (i.e., finding the target object and executing the required action). The comparison between two experimental conditions, in one of which the agents are required to ignore rather than to indicate objects, shows that the composition of the behavioral set significantly influences the development of compositional semantic structures.

关键词： Artificial neural networks behavior generalization compositional semantics evolutionary robotics

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Evolution of neural controllers for competitive game playing with teams of mobile robots

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robotics AND AUTONOMOUS SYSTEMS 2004年第3期46卷 135-150页

作者： Nelson, AL Grant, E Henderson, TC N Carolina State Univ Dept Elect & Comp Engn Ctr Robot & Intelligent Machines Raleigh NC 27695 USA Univ Utah Sch Comp Salt Lake City UT 84112 USA

In this work, we describe the evolutionary training of artificial neural network controllers for competitive team game playing behaviors by teams of real mobile robots. This research emphasized the development of methods to automate the production of behavioral robot controllers. We seek methods that do not require a human designer to define specific intermediate behaviors for a complex robot task. The work made use of a real mobile robot colony (evolutionary roBOTs) and a closely coupled computer-based simulated training environment. The acquisition of behavior in an evolutionary robotics system was demonstrated using a robotic version of the game Capture the Flag. In this game, played by two teams of competing robots, each team tries to defend its own goal while trying to 'attack' another goal defended by the other team. Robot neural controllers relied entirely on processed video data for sensing of their environment. Robot controllers were evolved in a simulated environment using evolutionary training algorithms. In the evolutionary process, each generation consisted of a competitive tournament of games played between the controllers in an evolving population. Robot controllers were selected based on whether they won or lost games in the course of a tournament. Following a tournament, the neural controllers were ranked competitively according to how many games they won and the population was propagated using a mutation and replacement strategy. After several hundred generations, the best performing controllers were transferred to teams of real mobile robots, where they exhibited behaviors similar to those seen in simulation including basic navigation, the ability to distinguish between different types of objects, and goal tending behaviors. (C) 2004 Elsevier B.V. All rights reserved.

关键词： evolutionary robotics robot colonies mobile robots evolutionary neural computing behavioral robotics

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Emergence of communication in embodied agents evolved for the ability to solve a collective navigation problem

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CONNECTION SCIENCE 2007年第1期19卷 53-74页

作者： Marocco, D. Nolfi, S. CNR Inst Cognit Sci & Technol I-00137 Rome Italy

In this paper. we present the results of an experiment in which a collection of simulated robots that have been evolved for the ability to solve a collective navigation problem develop a communication system that allows them to co-operate better. The analysis of the results obtained indicates how evolving robots develop a non-trivial communication system and exploit different communication modalities. The results also indicate how the possibility of co-adapting the robots' individual and social/communicative behaviour plays a key role in the development of progressively more complex and effective individuals.

关键词： evolutionary robotics emergence of communication collective robotics symbol grounding

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