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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Kargar, Babak Hosseinkhani Miras, Karine Eiben, A. E. Vrije Univ Amsterdam De Boelelaan 1105 NL-1081 HV Amsterdam Netherlands

ISBN: (纸本)9783031700705;9783031700712

We investigate an evolvable robot system where the body provides proprioceptive sensory signals to the controller (brain) about the positions of the joints. The key aspect we consider is whether all joints should be sensed or if sensing fewer joints would be better. We research this matter based on a test suite of twenty-two robots with various shapes and sizes and implement a system where the controller and the sensory signal system evolve together. Experiments with this system show that the evolved solutions use signals only from a fraction of the joints (25-51%) and perform better than the baseline, where all signals are used. This effect was observed across the majority of the test suite.

关键词： evolutionary robotics Proprioception Efficiency

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AutoMoDe-Pomodoro: An evolutionary Class of Modular Designs 22

AutoMoDe-Pomodoro: An Evolutionary Class of Modular Designs

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Genetic and evolutionary Computation Conference (GECCO)

作者： Cambier, Nicolas Ferrante, Eliseo Vrije Univ Amsterdam Computat Intelligence Grp Amsterdam Netherlands Technol Innovat Inst Abu Dhabi U Arab Emirates

ISBN: (纸本)9781450392686

In this paper, we reintroduce evolutionary algorithms into Auto-MoDe, an automatic design approach which optimizes behavioural modules into a probabilistic finite automaton. We evaluate three approaches, with different encodings of the probabilistic finite automaton phenotype, and observe their performances. This work opens modular designs to more advanced evolutionary robotics methods, such as novelty search and embodied evolution.

关键词： swarm robotics evolutionary robotics modular design genetic algorithm

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Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms 18th

Emergence of Specialised Collective Behaviors in Evolving He...

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18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： van Diggelen, Fuda de Carlo, Matteo Cambier, Nicolas Ferrante, Eliseo Eiben, Guszti Vrije Univ Amsterdam Boelelaan 1111 Amsterdam Netherlands

ISBN: (纸本)9783031700675;9783031700682

Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability;2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.

关键词： Swarm robotics evolutionary robotics Heterogeneous swarm

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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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Entropy-Based Local Fitnesses for evolutionary Multiagent Systems 22

Entropy-Based Local Fitnesses for Evolutionary Multiagent Sy...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Aydeniz, Ayhan Alp Nickelson, Anna Tumer, Kagan Oregon State Univ Collaborat Robot & Intelligent Syst Inst Corvallis OR 97331 USA

ISBN: (纸本)9781450392686

evolutionary multiagent systems have been successfully applied to many real world problems, including search and rescue and ocean exploration. However, as the number of agents increases in such problems, the evaluation function captures an individual agent's fitness less and less accurately. As a consequence, agents adopt a small set of acceptable behaviors that are neither optimal nor robust to environmental changes or teammate failures. Fitness shaping, intrinsic fitnesses, or multi-fitness learning alleviate some of these concerns but generally require domain knowledge or the functional form of the evaluation function. In this paper, we introduce Entropy-Based Local Fitnesses (EBLFs) that generate diverse behaviors for agents and produce robust team behaviors without requiring environmental knowledge. The key contribution of EBLFs is to inject a dense, entropy-based fitness into the agents' evolution without interfering with the sparse, high-level system evaluation function. Our results show that the agents using EBLFs learn new skills in difficult environments with sparse feedback without requiring domain knowledge. In addition, EBLFs generated new team-level behaviors that were not defined by a human operator, but beneficial to robust team performance.

关键词： evolutionary robotics Multi-agent systems Neuroevolution

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Investigating Premature Convergence in Co-optimization of Morphology and Control in Evolved Virtual Soft Robots 27th

Investigating Premature Convergence in Co-optimization of Mo...

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27th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar Conference27th European Conference on Genetic Programming (EuroGP)

作者： Mertan, Alican Cheney, Nick Univ Vermont Burlington VT 05401 USA

ISBN: (纸本)9783031569562;9783031569579

Evolving virtual creatures is a field with a rich history and recently it has been getting more attention, especially in the soft robotics domain. The compliance of soft materials endows soft robots with complex behavior, but it also makes their design process unintuitive and in need of automated design. Despite the great interest, evolved virtual soft robots lack the complexity, and co-optimization of morphology and control remains a challenging problem. Prior work identifies and investigates a major issue with the co-optimization process - fragile co-adaptation of brain and body resulting in premature convergence of morphology. In this work, we expand the investigation of this phenomenon by comparing learnable controllers with proprioceptive observations and fixed controllers without any observations, whereas in the latter case, we only have the optimization of the morphology. Our experiments in two morphology spaces and two environments that vary in complexity show, concrete examples of the existence of high-performing regions in the morphology space that are not able to be discovered during the co-optimization of the morphology and control, yet exist and are easily findable when optimizing morphologies alone. Thus this work clearly demonstrates and characterizes the challenges of optimizing morphology during co-optimization. Based on these results, we propose a new body-centric framework to think about the co-optimization problem which helps us understand the issue from a search perspective. We hope the insights we share with this work attract more attention to the problem and help us to enable efficient brain-body co-optimization.

关键词： evolutionary robotics Soft robotics Brain-body co-optimization

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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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Adaptive Team Cooperative Co-Evolution for a Multi-Rover Distribution Problem 23

Adaptive Team Cooperative Co-Evolution for a Multi-Rover Dis...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Fontbonne, Nicolas Maudet, Nicolas Bredeche, Nicolas Sorbonne Univ CNRS ISIR LIP6 F-F75005 Paris France Sorbonne Univ LIP6 F-75005 Paris France Sorbonne Univ CNRS ISIR F-75005 Paris France

ISBN: (纸本)9798400701191

This paper deals with policy learning for a team of heterogeneous robotic agents when the whole team shares a single reward. We address the problem of providing an accurate estimation of the contribution of each agent in tasks where coordination between agents requires joint policy updates of two (or more) agents. This is typically the case when two agents must simultaneously modify their behaviors to perform a joint action that leads to a performance gain for the whole team. We propose a cooperative co-evolutionary algorithm extended with a multi-armed bandit algorithm that dynamically adjusts the number of agents that should update their policies simultaneously, aiming both for performance and learning speed. We use a realistic robotic multi-rover task where agents must physically distribute themselves on points of interest of different natures to complete the task. Results show that the algorithm is able to select the best group size for policy updates that reflects the task's coordination requirements. Surprisingly, we also reveal that coupling between agents' actions in a realistic setup can also emerge from interactions at the phenotypical level, hinting at subtle interactions during learning between the control parameter space and the behavioral space.

关键词： multi-robots multi-agent systems evolutionary robotics multiagent reinforcement learning multi-armed bandits marginal contribution fitness evaluation cooperative coevolution EA

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Toward an Empirical Practice in Offline Fully Automatic Design of Robot Swarms

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2022年第6期26卷 1236-1245页

作者： Ligot, Antoine Cotorruelo, Andres Garone, Emanuele Birattari, Mauro Univ Libre Bruxelles IRIDIA B-1050 Brussels Belgium Univ Libre Bruxelles Serv Automatique & Anal Syst B-1050 Brussels Belgium

Due to the lack of systematic empirical analyses and comparisons of ideas and methods, a clearly established state of the art is still missing in the optimization-based design of robot swarms. In this article, we propose an experimental protocol for the comparison of fully automatic design methods. This protocol is characterized by two notable elements: 1) a way to define benchmarks for the evaluation and comparison of design methods and 2) a sampling strategy that minimizes the variance when estimating their expected performance. To define generally applicable benchmarks, we introduce the notion of mission generator: a tool to generate missions that mimic those a design method will eventually have to solve. To minimize the variance of the performance estimation, we show that, under some common assumptions, one should adopt the sampling strategy that maximizes the number of missions considered-a formal proof is provided as the supplementary material. We illustrate the experimental protocol by comparing the performance of two offline fully automatic design methods that were presented in previous publications.

关键词： evolutionary robotics experimental methodology offline automatic design optimization-based design swarm robotics

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SafeAPT: Safe Simulation-to-Real Robot Learning Using Diverse Policies Learned in Simulation

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IEEE robotics AND AUTOMATION LETTERS 2022年第3期7卷 6838-6845页

作者： Kaushik, Rituraj Arndt, Karol Kyrki, Ville Aalto Univ Dept Elect Engn & Automat Intelligent Robot Grp Espoo 02150 Finland

The framework of sim-to-real learning, i.e., training policies in simulation and transferring them to real-world systems, is one of the most promising approaches towards data-efficient learning in robotics. However, due to the inevitable reality gap between the simulation and the real world, a policy learned in the simulation may not always generate a safe behaviour on the real robot. As a result, during policy adaptation in the real world, the robot may damage itself or cause harm to its surroundings. In this work, we introduce SafeAPT, a multi-goal robot learning algorithm that leverages a diverse repertoire of policies evolved in simulation and transfers the most promising safe policy to the real robot through episodic interaction. To achieve this, SafeAPT iteratively learns probabilistic reward and safety models from real-world observations using simulated experiences as priors. Then, it performs Bayesian optimization to select the best policy from the repertoire with the reward model, while maintaining the specified safety constraint using the safety model. SafeAPT allows a robot to adapt to a wide range of goals safely with the same repertoire of policies evolved in the simulation. We compare SafeAPT with several baselines, both in simulated and real robotic experiments, and show that SafeAPT finds high-performing policies within a few minutes of real-world operation while minimizing safety violations during the interactions.

关键词： evolutionary robotics learning from experience machine learning for robot control

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