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Cooperative Co-evolution and Adaptive Team Composition for a Multi-rover Resource Allocation Problem 1

25th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar Conference

作者： Fontbonne, Nicolas Maudet, Nicolas Bredeche, Nicolas Sorbonne Univ ISIR CNRS F-75005 Paris France Sorbonne Univ LIP6 F-75005 Paris France

ISBN: (数字)9783031020568

ISBN: (纸本)9783031020568;9783031020551

In this paper, we are interested in ad hoc autonomous agent team composition using cooperative co-evolutionary algorithms (CCEA). In order to accurately capture the individual contribution of team agents, we propose to limit the number of agents which are updated in-between team evaluations. However, this raises two important problems with respect to (1) the cost of accurately estimating the marginal contribution of agents with respect to the team learning speed and (2) completing tasks where improving team performance requires multiple agents to update their policies in a synchronized manner. We introduce a CCEA algorithm that is capable of learning how to update just the right amount of agents' policies for the task at hand. We use a variation of the El Farol Bar problem, formulated as a multi-robot resource selection problem, to provide an experimental validation of the algorithms proposed.

关键词： ad hoc autonomous agent teams multi-agent systems marginal contribution team composition multi-robots cooperative co-evolutionary algorithms (CCEA) evolutionary computation evolutionary robotics

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Minimizing torque requirements in robotic manipulation through elastic elements optimization in a physics engine

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2024年第1期21卷

作者： Marchal, Maxime Marzougui, Dries Furnemont, Raphael Verstraten, Tom Wyffels, Francis Vrije Univ Brussel Fac Mech Engn Multibody Mech Res Grp Ixelles Belgium Flanders Make Ghent Belgium Ghent Univ imec IDLab AIRO Res lab Antwerp Belgium Vrije Univ Brussel Fac Mech Engn Multibody Mech Res Grp B-1050 Elsene Belgium

The increasing number of robots and the rising cost of electricity have spurred research into energy-reducing concepts in robotics. One such concept, elastic actuation, introduces compliant elements such as springs into the robot structure. This article presents a comparative analysis between two types of elastic actuation, namely, monoarticular parallel elastic actuation and biarticular parallel elastic actuation, and demonstrates an end-to-end pipeline for their optimization. Starting from the real-world system identification of an RRR robotic arm, we calibrate a simulation model in a general-purpose physics engine and employ in silico evolutionary optimization to co-optimize spring configurations and trajectories for a pick-and-place task. Finally, we successfully transfer the in silico optimized elastic elements and trajectory to the real-world prototype. Our results substantiate the ability of elastic actuation to reduce the actuators' torque requirements heavily. In contrast to previous work, we highlight the superior performance of the biarticular variant over the monoarticular configuration. Furthermore, we show that a combination of both proves most effective. This work provides valuable insights into the torque-reducing use of elastic actuation and demonstrates an actuator-invariant in silico optimization methodology capable of bridging the sim2real gap.

关键词： Actuation and joint mechanisms compliant joints and mechanisms evolutionary robotics methods and tools for robot system design optimization and optimal control

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Out of Time: On the Constrains that Evolution in Hardware Faces When Evolving Modular Robots 25th

Out of Time: On the Constrains that Evolution in Hardware Fa...

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25th International Conference on the Applications of evolutionary Computation (EvoApplications) Held as Part of EvoStar Conference

作者： Moreno, Rodrigo Faina, Andres IT Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9783031024627;9783031024610

With the recent advances of modular robots and low-cost manipulators, the evolution of robots, including morphologies and controllers, has become possible to perform in a physical setup without using any simulators. In this scenario, the evolution cannot be parallelized and the wall time becomes a scarce resource that should be used wisely. This paper analyses different algorithms by using the wall time as a stopping criterion for evolution, and it takes into account that wall time depends on the evaluation time plus the time to assemble and disassemble robots before and after an evaluation. The experiments have been performed in simulation, but the evaluation and assembly time have been carefully modelled from previous hardware experiments. Results suggest that (i) genetic algorithms are severely penalized, (ii) genetic algorithms can be improved by performing several evaluations of controllers for each morphology, and that (iii) evolutionary strategies that can chain several evaluations of robots with close morphologies can outperform other evolutionary algorithms. This finding is not surprising, but to the best of our knowledge previous attempts to evolve modular robots in hardware have not employed evolutionary strategies.

关键词： Modular robots evolutionary robotics Morphological evolution Evolution in hardware

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On the Impact of Body Material Properties on Neuroevolution for Embodied Agents: the Case of Voxel-based Soft Robots 22

On the Impact of Body Material Properties on Neuroevolution ...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Medvet, Eric Nadizar, Giorgia Pigozzi, Federico Univ Trieste DIA Trieste Italy Univ Trieste DMG Trieste Italy

ISBN: (纸本)9781450392686

Artificial agents required to perform non-trivial tasks are commonly controlled with Artificial Neural Networks (ANNs), which need to be carefully fine-tuned. This is where ANN optimization comes into play, often in the form of Neuroevolution (NE). Among artificial agents, the embodied ones are characterized by a strong body-brain entanglement, i.e., a strong interdependence between the physical properties of the body and the controller. In this work, we aim at characterizing said interconnection, experimentally evaluating the impact body material properties have on NE for embodied agents. We consider the case of Voxel-based Soft Robots (VSRs), a class of simulated modular soft robots which achieve movement through the rhythmical contraction and expansion of their modules. We experiment varying several physical properties of VSRs and assess the effectiveness of the evolved controllers for the task of locomotion, together with their robustness and adaptability. Our results confirm the existence of a deep body-brain interrelationship for embodied agents, and highlight how NE fruitfully exploits the physical properties of the agents to give rise to a wide gamut of effective and adaptable behaviors.

关键词： evolutionary robotics Embodied Intelligence Neuroevolution Soft robotics Adaptability

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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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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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A Comparative Analysis on Genome Pleiotropy for Evolved Soft Robots 22

A Comparative Analysis on Genome Pleiotropy for Evolved Soft...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Marzougui, Dries Biondina, Matthijs Wyffels, Francis Univ Ghent Ghent Belgium

ISBN: (纸本)9781450392686

Biological evolution shapes the body and brain of living creatures together over time. By contrast, in evolutionary robotics, the co-optimization of these subsystems remains challenging. Conflicting mutations cause dissociation between morphology and control, which leads to premature convergence. Recent works have proposed algorithmic modifications to mitigate the impact of conflicting mutations. However, the importance of genetic design remains underexposed. Current approaches are divided between a single, pleiotropic genetic encoding and two isolated encodings representing morphology and control. This design choice is commonly made ad hoc, causing a lack of consistency for practitioners. To standardize this design, we performed a comparative analysis between these two configurations on a soft robot locomotion task. Additionally, we incorporated two currently unexplored alternatives that drive these configurations to their logical extremes. Our results demonstrate that pleiotropic representations yield superior performance in fitness and robustness towards premature convergence. Moreover, we showcase the importance of shared structure in the pleiotropic representation of robot morphology and control to achieve this performance gain. These findings provide valuable insights into genetic encoding design, which supply practitioners with a theoretical foundation to pursue efficient brain-body co-optimization.

关键词： evolutionary robotics Representation Soft robotics Genetic encodings CPPN Brain-body co-optimization

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Joint Mechanical Design and Flight Control Optimization of a Nature-Inspired Unmanned Aerial Vehicle via Collaborative Co-Evolution

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IEEE robotics AND AUTOMATION LETTERS 2021年第2期6卷 2044-2051页

作者： Sufiyan, Danial Win, Luke Soe Thura Win, Shane Kyi Hla Soh, Gim Song Foong, Shaohui Singapore Univ Technol & Design SUTD Engn Prod Dev Pillar Singapore 487372 Singapore

In this work, a collaborative co-evolution approach is adopted to solve a joint physical design and feedback control optimization problem of a nature-inspired Unmanned Aerial Vehicle (UAV). Unlike traditional multirotors and fixed-wing aircraft, lift is achieved by spinning its entire body with attached aerofoils around a central axis and positional control is attained through regulation of 2 sets of independent aerodynamic surfaces and thrusters. The collaborative co-evolution process consists of 2 'species,' the first consisting of the mechanical design variables and the second consisting of Proportional-Integral-Derivative (PID) and central pattern generator (CPG) controller variables. Each species have their own respective individual evolutionary Algorithm (EA) solvers, Covariance Matrix Adaptation-evolutionary Strategy (CMA-ES) and Parameter Exploring Policy Gradients (PEPG). In each optimization iteration, the parameters of one species is combined with representatives with the highest fitness from the other species and fed into a shared model for fitness evaluation, with each species taking turns to send a representative. Detailed performance comparison in trajectory tracking and power consumption between the proposed jointly optimized system against a design-only optimized, control-only optimized and unoptimized baseline were conducted. It was found that configurations with optimized designs would draw on average 18% less power than the non-optimized designs, and configurations with optimized controllers reduce error by 56% on average. The best performing configuration is the one with jointly optimized mechanical design and controller which outperforms all other configurations individually and collectively.

关键词： Aerial systems mechanics and control evolutionary robotics methods and tools for robot system design

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Environmental Adaptation of Robot Morphology and Control Through Real-World Evolution

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evolutionary COMPUTATION 2021年第4期29卷 441-461页

作者： Nygaard, T. F. Martin, C. P. Howard, D. Torresen, J. Glette, K. Univ Oslo Dept Informat Oslo Norway Norwegian Def Res Estab Kjeller Norway Australian Natl Univ Res Sch Comp Sci Canberra ACT Australia CSIRO Cyber Phys Syst Program St Lucia Qld Australia Univ Oslo Dept Informat RITMO Oslo Norway

Robots operating in the real world will experience a range of different environments and tasks. It is essential for the robot to have the ability to adapt to its surroundings to work efficiently in changing conditions. evolutionary robotics aims to solve this by optimizing both the control and body (morphology) of a robot, allowing adaptation to internal, as well as external factors. Most work in this field has been done in physics simulators, which are relatively simple and not able to replicate the richness of interactions found in the real world. Solutions that rely on the complex interplay among control, body, and environment are therefore rarely found. In this article, we rely solely on real-world evaluations and apply evolutionary search to yield combinations of morphology and control for our mechanically self-reconfiguring quadruped robot. We evolve solutions on two distinct physical surfaces and analyze the results in terms of both control and morphology. We then transition to two previously unseen surfaces to demonstrate the generality of our method. We find that the evolutionary search finds high-performing and diverse morphology-controller configurations by adapting both control and body to the different properties of the physical environments. We additionally find that morphology and control vary with statistical significance between the environments. Moreover, we observe that our method allows for morphology and control parameters to transfer to previously unseen terrains, demonstrating the generality of our approach.

关键词： evolutionary robotics legged locomotion evolutionary computation

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Time efficiency in optimization with a bayesian-evolutionary algorithm

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SWARM AND evolutionary COMPUTATION 2022年 69卷 100970-100970页

作者： Lan, Gongjin Tomczak, Jakub M. Roijers, Diederik M. Eiben, A. E. Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Guangdong Peoples R China Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Vrije Univ Brussel AI Lab Brussels Belgium HU Univ Appl Sci Utrecht Inst ICT Utrecht Netherlands

Not all generate-and-test search algorithms are created equal. Bayesian Optimization (BO) invests a lot of computation time to generate the candidate solution that best balances the predicted value and the uncertainty given all previous data, taking increasingly more time as the number of evaluations performed grows. evolutionary Algorithms (EA) on the other hand rely on search heuristics that typically do not depend on all previous data and can be done in constant time. Both BO and EA community typically assess their performance as a function of the number of evaluations, i.e., data efficiency. However, this is unfair once we start to compare the efficiency of these classes of algorithms, as the overhead times to generate candidate solutions are significantly different . We suggest to measure the efficiency of generate-and-test search algorithms as the expected gain in the objective value per unit of computation time spent, i.e., time efficiency. To the time-efficient search algorithm, we therefore propose a new algorithm, a combination of BO and an EA, BEA for short, that starts with BO, then transfers knowledge to an EA, and subsequently runs the EA. We compare the BEA with BO, the EA, Differential Evolution (DE), and Particle Swarm Optimization (PSO). The results show that BEA outperforms BO, the EA, DE and PSO in terms of time efficiency, and ultimately leads to better performance on well-known benchmark objective functions with many local optima. Moreover, we test BEA, BO, and the EA on nine test cases of robot learning problems and here again we find that BEA outperforms the other algorithms.

关键词： Optimization algorithms Time efficiency Bayesian optimization evolutionary algorithm Differential evolution Particle swarm optimization evolutionary robotics

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