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How Perception, Actuation, and Communication Impact the Emergence of Collective Intelligence in Simulated Modular Robots

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ARTIFICIAL LIFE 2024年第4期30卷 448-465页

作者： Rusin, Francesco Medvet, Eric Univ Trieste Dept Math & Geosci Trieste Italy Univ Trieste Dept Engn & Architecture Trieste Italy

Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions;nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task.

关键词： Embodied AI collective intelligence evolutionary robotics neuroevolution modularity

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Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年第2期15卷 507-517页

作者： Le Goff, Leni K. Buchanan, Edgar Hart, Emma Eiben, Agoston E. Li, Wei De Carlo, Matteo Winfield, Alan F. Hale, Matthew F. Woolley, Robert Angus, Mike Timmis, Jon Tyrrell, Andy M. Edinburgh Napier Univ Sch Comp Edinburgh EH11 4BN Midlothian Scotland Univ York Dept Elect Engn York N Yorkshire England Vrije Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands Univ West England Bristol Robot Lab Bristol BS16 1QY England Univ Sunderland Sch Comp Sci Sunderland England

Most work in evolutionary robotics centers on evolving a controller for a fixed body plan. However, previous studies suggest that simultaneously evolving both controller and body plan could open up many interesting possibilities. However, the joint optimization of body plan and control via evolutionary processes can be challenging in rich morphological spaces. This is because offspring can have body plans that are very different from either of their parents, leading to a potential mismatch between the structure of an inherited neural controller and the new body. To address this, we propose a framework that combines an evolutionary algorithm to generate body plans and a learning algorithm to optimize the parameters of a neural controller. The topology of this controller is created once the body plan of each offspring has been generated. The key novelty of the approach is to add an external archive for storing learned controllers that map to explicit "types" of robots (where this is defined with respect to the features of the body plan). By initiating learning from a controller with an appropriate structure inherited from the archive, rather than from a randomly initialized one, we show that both the speed and magnitude of learning increase over time when compared to an approach that starts from scratch, using two tasks and three environments. The framework also provides new insights into the complex interactions between evolution and learning.

关键词： Robots Optimization Topology Statistics Sociology Process control Aerospace electronics Embodied intelligence evolutionary robotics

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Reconfigurable, Multi-Material, Voxel-Based Soft Robots

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IEEE robotics AND AUTOMATION LETTERS 2023年第3期8卷 1255-1262页

作者： Legrand, Julie Terryn, Seppe Roels, Ellen Vanderborght, Bram Vrije Univ Brussel Robot & Multibody Mech R&MM Res Grp B-1050 Brussels Belgium Imec B-1050 Brussels Belgium

Voxel-based robots are aggregations of soft and simple building blocks that have been extensively evolved and simulated to perform various tasks, like walking, jumping or swimming. However, real-life voxel-based robots are rather scarce because of their challenging design and assembly. With the current materials and assembling methods, the interfaces between the soft multi-material voxels are prone to failure. This work proposes to make voxels out of reversible Diels-Alder polymers, which are available in a broad range of mechanical properties. By doing so, the covalent bonds at the multi-material interface ensure strong chemical connections, while allowing for reconfiguration. A first voxel-based gripper is thus robustly assembled, then disassembled, using its pieces (voxels) for reassembling another robot, i.e. a voxel-based walking robot. This reconfigurable property allows iterative validation of the simulated voxel-based robots and fine-tuning of the simulations parameters in a sustainable and economical way. Both physical voxel-based robots show similar behaviors as their simulations with root-mean-square errors down to 10.4%.

关键词： Plastics Legged locomotion Robot kinematics Soft robotics Grippers Heating systems Faces evolutionary robotics methods and tools for robot system design soft robot materials and design

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Evolving Hebbian Learning Rules in Voxel-Based Soft Robots

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年第3期15卷 1536-1546页

作者： Ferigo, Andrea Iacca, Giovanni Medvet, Eric Pigozzi, Federico Univ Trento Dept Informat Engn & Comp Sci I-38123 Trento Italy Univ Trieste Dept Engn & Architecture I-34127 Trieste Italy

According to Hebbian theory, synaptic plasticity is the ability of neurons to strengthen or weaken the synapses among them in response to stimuli. It plays a fundamental role in the processes of learning and memory of biological neural networks. With plasticity, biological agents can adapt on multiple timescales and outclass artificial agents, the majority of which still rely on static artificial neural network (ANN) controllers. In this work, we focus on voxel-based soft robots ( VSRs), a class of simulated artificial agents, composed as aggregations of elastic cubic blocks. We propose a Hebbian ANN controller where every synapse is associated with a Hebbian rule that controls the way the weight is adapted during the VSR lifetime. For a given task and morphology, we optimize the controller for the task of locomotion by evolving, rather than the weights, the parameters of Hebbian rules. Our results show that the Hebbian controller is comparable, often better than a non-Hebbian baseline and that it is more adaptable to damages. We also provide novel insights into the inner workings of plasticity and demonstrate that "true" learning does take place, as the evolved controllers improve over the lifetime and generalize well.

关键词： evolutionary robotics Hebbian learning synaptic plasticity voxel-based soft robots (VSRs)

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Generating collective behavior of a robotic swarm using an attention agent with deep neuroevolution

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ARTIFICIAL LIFE AND robotics 2023年第4期28卷 669-679页

作者： Iwami, Arumu Morimoto, Daichi Shiozaki, Naoya Hiraga, Motoaki Ohkura, Kazuhiro Hiroshima Univ Hiroshima Japan Kyoto Inst Technol Kyoto Japan

This paper focuses on generating collective behavior of a robotic swarm using an attention agent. The selective attention mechanism enables an agent to cope with environmental variations which are irrelevant to the task. This paper applies attention mechanisms to a robotic swarm for enhancing system-level properties, such as flexibility or scalability. To train an attention agent, evolutionary computations become a promising method, because a controller structure is not restricted by a gradient-based method. Therefore, this paper employs a deep neuroevolution approach to generating collective behavior in a robotic swarm. The experiments are conducted by computer simulations that consist of the Unity 3D game engine. The performance of the attention agent is compared with the convolutional neural network approach. The experimental results showed that the attention agent obtained generalization abilities in a robotic swarm similar to single-agent problems.

关键词： Swarm robotics evolutionary robotics Deep neuroevolution Convolutional neural network Attention agent

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Understanding fitness landscapes in morpho-evolution via local optima networks

Understanding fitness landscapes in morpho-evolution via loc...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Thomson, Sarah L. Le Goff, Leni Hart, Emma Buchanan, Edgar Edinburgh Napier Univ Edinburgh Midlothian Scotland Univ York York N Yorkshire England

ISBN: (纸本)9798400704949

Morpho-Evolution (ME) refers to the simultaneous optimisation of a robot's design and controller to maximise performance given a task and environment. Many genetic encodings have been proposed which are capable of representing design and control. Previous research has provided empirical comparisons between encodings in terms of their performance with respect to an objective function and the diversity of designs that are evaluated, however there has been no attempt to explain the observed findings. We address this by applying Local Optima Network (LON) analysis to investigate the structure of the fitness landscapes induced by three different encodings when evolving a robot for a locomotion task, shedding new light on the ease by which different fitness landscapes can be traversed by a search process. This is the first time LON analysis has been applied in the field of ME despite its popularity in combinatorial optimisation domains;the findings will facilitate design of new algorithms or operators that are customised to ME landscapes in the future.

关键词： fitness landscape analysis evolutionary robotics local optima networks indirect representation

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Echo state networks for embodied evolution in robotic swarms

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ARTIFICIAL LIFE AND robotics 2023年第1期28卷 139-147页

作者： Hiraga, Motoaki Katada, Yoshiaki Ohkura, Kazuhiro Hiroshima Univ Grad Sch Adv Sci & Engn 1-4-1 Kagamiyama Higashihiroshima Hiroshima 7398527 Japan Setsunan Univ Dept Elect & Elect Engn 17-8 Ikedanaka Machi Neyagawa Osaka 5728508 Japan

Embodied evolution is an evolutionary robotics approach that implements an evolutionary algorithm over a population of robots and evolves while the robots perform their tasks. So far, most studies on embodied evolution utilize relatively simple neural networks as robot controllers. However, a simple structured controller might restrict robot behavior and lead to lower performance. This paper proposes an embodied evolution approach that uses echo state networks as robot controllers. The experiments are conducted using computer simulations, and the controllers are evolved in a two-target navigation task. The results show that the echo state network controllers outperform the conventional controllers.

关键词： Embodied evolution Swarm robotics evolutionary robotics Echo state networks

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Towards Multi-Morphology Controllers with Diversity and Knowledge Distillation

Towards Multi-Morphology Controllers with Diversity and Know...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Mertan, Alican Cheney, Nick Univ Vermont Neurobot Lab Burlington VT 05405 USA

ISBN: (纸本)9798400704949

Finding controllers that perform well across multiple morphologies is an important milestone for large-scale robotics, in line with recent advances via foundation models in other areas of machine learning. However, the challenges of learning a single controller to control multiple morphologies make the 'one robot one task' paradigm dominant in the field. To alleviate these challenges, we present a pipeline that: (1) leverages Quality Diversity algorithms like MAP-Elites to create a dataset of many single-task/single-morphology teacher controllers, then (2) distills those diverse controllers into a single multi-morphology controller that performs well across many different body plans by mimicking the sensory-action patterns of the teacher controllers via supervised learning. The distilled controller scales well with the number of teachers/morphologies and shows emergent properties. It generalizes to unseen morphologies in a zero-shot manner, providing robustness to morphological perturbations and instant damage recovery. Lastly, the distilled controller is also independent of the teacher controllers - we can distill the teacher's knowledge into any controller model, making our approach synergistic with architectural improvements and existing training algorithms for teacher controllers.

关键词： evolutionary robotics soft robotics brain-body co-optimization

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Robustness in Diversity: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits 7

Robustness in Diversity: Quality-Diversity Driven Discovery ...

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7th International Conference on Soft robotics (ROBOSOFT)

作者： Daly, John Casper, Daniel Farooq, Muhammad James, Andrew Khan, Ali Mulgrew, Phoenix Tyebkhan, Daniel Vo, Bao Rieffel, John Union Coll Schenectady NY 12308 USA Union Coll Dept Comp Sci Schenectady NY 12308 USA

ISBN: (纸本)9798350381818

Soft robotics aims to develop robots able to adapt their behavior across a wide range of unstructured and unknown environments. A critical challenge of soft robotic control is that nonlinear dynamics often result in complex behaviors that are hard to model and predict. Typically behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. More recently, optimization algorithms such as Genetic Algorithms (GA) have been used to discover gaits, but these behaviors are often optimized for a single environment or terrain, and can be brittle to unplanned changes to terrain. In this paper we demonstrate how Quality Diversity Algorithms, which search of a range of high-performing behaviors, can produce repertoires of gaits that are robust to changing terrains. This robustness significantly out-performs that of gaits produced by a single objective optimization algorithm.

关键词： Soft robotics Quality Diversity Algorithms evolutionary robotics

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Generalizations of Evolved Decision-Making Mechanisms in Swarm Collective Perception 17th

Generalizations of Evolved Decision-Making Mechanisms in Swa...

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17th Italian Workshop on Artificial Life and evolutionary Computation (WIVACE)

作者： Trendafilov, Dari Almansoori, Ahmed Carletti, Timoteo Tuci, Elio Univ Namur Namur Inst Complex Syst Namur Belgium

ISBN: (纸本)9783031574290;9783031574306

This paper presents a study investigating the generalization characteristics of two neuro-controllers underpinning decision-making mechanisms in a swarm of robots engaged in a collective perception task. The neuro-controllers are both designed-using evolutionary computation-to operate in a randomly distributed cues environment, but under different conditions for what concerns the length of the communication range characterising the robots interactions. For one neuro-controller, the communication range during the design phase is 30 cm, while for the other is 50 cm. The aim of the study is to explore the robustness and the limitations of the two distinct neuro-controllers across a range of different conditions and to establish the optimal bounds on the swarm communication range for this collective perception task. To examine the performance of the two neuro-controllers we conduct a series of post-evaluations in 45 distinct environments, given by nine different distributions of the perceptual cues, and five different communication ranges (i.e., 10, 20, 30, 40, and 50 cm). The results demonstrate that the neuro-controller evolved with a swarm communication range of 30 cm generalizes better and exceeds the performance of the other neuro-controller evolved with 50 cm communication range in a vast majority of the post-evaluation conditions. However, the swarm performance degrades in conditions with patchily distributed perceptual cues and/or very short communication range.

关键词： Swarm robotics Collective perception evolutionary robotics

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