In this study, we review robots behavior especially warrior robots by using evolutionary algorithms. This kind of algorithms is inspired by nature that causes robots behaviors get resemble to collective behavior. Collective behavior of creatures such as bees was shown that do some functions which depended on interaction and cooperation would need to a well-organized system so that all creatures within it carry out their duty, very well. For robots which do not have any intelligence, we can define an algorithm and show the results by a simple simulation.
Robots Need Some Education: On the complexity of learning in evolutionary robotics
University of Oslo（奥斯陆大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Leiden University（莱顿大学）；Edinburgh Napier University（爱丁堡龙比亚大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
This paper presents an artificial evolutionbased method for stereo image analysis and its application to real-time obstacle detection and avoidance for a mobile robot. It uses the Parisian approach, which consists here in splitting the representation of the robot's environment into a large number of simple primitives, the "flies", which are evolved following a biologically inspired scheme and give a fast, low-cost solution to the obstacle detection problem in mobile robotics.
A popular paradigm in robotic learning is to train a policy from scratch for every new robot. This is not only inefficient but also often impractical for complex robots. In this work, we consider the problem of transferring a policy across two different robots with significantly different parameters such as kinematics and morphology. Existing approaches that train a new policy by matching the action or state transition distribution, including imitation learning methods, fail due to optimal action and/or state distribution being mismatched in different robots. In this paper, we propose a novel …
Generative Evolutionary Design of Voxel-Based Soft Robots with Provable Optimality
Shanghai Jiao Tong University（上海交通大学）；Shanghai Innovation Institute（上海创新研究院）；Renmin University of China（中国人民大学）；Intelligent Game and Decision Laboratory（智能博弈与决策实验室）
Shaping the Evolutionary Dynamics of Robot Morphology via Adaptive Control Learning
Shanghai Jiao Tong University（上海交通大学）；Intelligent Game and Decision Laboratory（智能游戏与决策实验室）；Renmin University of China（中国人民大学）；Shanghai Innovation Institute（上海创新研究院）
Université de Lorraine（洛林大学）；INRIA Nancy Grand-Est（法国国家数字科学与技术研究所南锡大东部大区中心）
In this paper, we study the impact of selection methods in the context of on-line on-board distributed evolutionary algorithms. We propose a variant of the mEDEA algorithm in which we add a selection operator, and we apply it in a taskdriven scenario. We evaluate four selection methods that induce different intensity of selection pressure in a multi-robot navigation with obstacle avoidance task and a collective foraging task. Experiments show that a small intensity of selection pressure is sufficient to rapidly obtain good performances on the tasks at hand. We introduce different measures to c…
The semi-automatic or automatic synthesis of robot controller software is both desirable and challenging. Synthesis of rather simple behaviors such as collision avoidance by applying artificial evolution has been shown multiple times. However, the difficulty of this synthesis increases heavily with increasing complexity of the task that should be performed by the robot. We try to tackle this problem of complexity with Artificial Homeostatic Hormone Systems (AHHS), which provide both intrinsic, homeostatic processes and (transient) intrinsic, variant behavior. By using AHHS the need for pre-def…
Roborobo! is a multi-platform, highly portable, robot simulator for large-scale collective robotics experiments. Roborobo! is coded in C++, and follows the KISS guideline ("Keep it simple"). Therefore, its external dependency is solely limited to the widely available SDL library for fast 2D Graphics. Roborobo! is based on a Khepera/ePuck model. It is targeted for fast single and multi-robots simulation, and has already been used in more than a dozen published research mainly concerned with evolutionary swarm robotics, including environment-driven self-adaptation and distributed evolutionary op…
The Role of Morphological Variation in Evolutionary Robotics: Maximizing Performance and Robustness
Federal University of Santa Catarina (UFSC)（圣卡塔琳娜联邦大学）；Institute of Cognitive Sciences and Technologies (ISTC), National Research Council (CNR)（国家研究委员会认知科学与技术研究所）
Simultaneously evolving morphologies (bodies) and controllers (brains) of robots can cause a mismatch between the inherited body and brain in the offspring. To mitigate this problem, the addition of an infant learning period by the so-called Triangle of Life framework has been proposed relatively long ago. However, an empirical assessment is still lacking to-date. In this paper we investigate the effects of such a learning mechanism from different perspectives. Using extensive simulations we show that learning can greatly increase task performance and reduce the number of generations required …
Institute for Data Science, Engineering, and Analytics, TH Köln（科隆应用技术大学数据科学、工程与分析研究所）；Department of Computer Science, Vrije Universiteit（自由大学计算机科学系）
Most evolutionary robotics studies focus on evolving some targeted behavior without taking the energy usage into account. This limits the practical value of such systems because energy efficiency is an important property for real-world autonomous robots. In this paper, we mitigate this problem by extending our simulator with a battery model and taking energy consumption into account during fitness evaluations. Using this system we investigate how energy awareness affects the evolution of robots. Since our system is to evolve morphologies as well as controllers, the main research question is tw…
Technische Universität Darmstadt（达姆施塔特工业大学）；Max-Planck Institute for Intelligent Systems（马克斯·普朗克智能系统研究所）
Despite recent progress in robot learning, it still remains a challenge to program a robot to deal with open-ended object manipulation tasks. One approach that was recently used to autonomously generate a repertoire of diverse skills is a novelty based Quality-Diversity~(QD) algorithm. However, as most evolutionary algorithms, QD suffers from sample-inefficiency and, thus, it is challenging to apply it in real-world scenarios. This paper tackles this problem by integrating a neural network that predicts the behavior of the perturbed parameters into a novelty based QD algorithm. In the proposed…
Inria（法国国家信息与自动化研究所）；CNRS（法国国家科学研究中心）；Université de Lorraine（洛林大学）；Imperial College London（帝国理工学院）
The recently introduced Intelligent Trial and Error algorithm (IT\&E) enables robots to creatively adapt to damage in a matter of minutes by combining an off-line evolutionary algorithm and an on-line learning algorithm based on Bayesian Optimization. We extend the IT\&E algorithm to allow for robots to learn to compensate for damages while executing their task(s). This leads to a semi-episodic learning scheme that increases the robot's lifetime autonomy and adaptivity. Preliminary experiments on a toy simulation and a 6-legged robot locomotion task show promising results.
Towards Deploying VLA without Fine-Tuning: Plug-and-Play Inference-Time VLA Policy Steering via Embodied Evolutionary Diffusion
Collaborative and Versatile Robots (CLOVER) Laboratory, T-Stone Robotics Institute, The Chinese University of Hong Kong, Hong Kong（协作与多功能机器人实验室，T-Stone机器人研究所，香港中文大学，香港）；Φ \Phi -Institute for Physical Human Intelligence（物理人机智能研究所）；Center for Embodied Artificial Intelligence and Computer Vision, Shenzhen Loop Area Institute, Shenzhen, China（具身人工智能与计算机视觉中心，深圳环园研究院，深圳，中国）；Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy（先进机器人系，意大利理工学院，热那亚，意大利）
University of Vermont（佛蒙特大学）；Yale University（耶鲁大学）；Tufts University（塔夫茨大学）
Robots deployed at orders of magnitude different size scales, and that retain the same desired behavior at any of those scales, would greatly expand the environments in which the robots could operate. However it is currently not known whether such robots exist, and, if they do, how to design them. Since self similar structures in nature often exhibit self similar behavior at different scales, we hypothesize that there may exist robot designs that have the same property. Here we demonstrate that this is indeed the case for some, but not all, modular soft robots: there are robot designs that exh…
Social Learning Strategies for Evolved Virtual Soft Robots
Department of Informatics, University of Oslo（奥斯陆大学信息学院）；University Toulouse Capitole, IRIT - CNRS UMR5505（图卢兹卡普利大学）；University of Trieste（特里埃斯特大学）；RITMO, University of Oslo（奥斯陆大学RITMO）
Sorbonne Université（索邦大学）；CNRS（法国国家科学研究中心）；Vrije Universiteit（阿姆斯特丹自由大学）；Universidade da Coruna（拉科鲁尼亚大学）
This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer …
Morphology, Evolution & Cognition Lab, University of Vermont（佛蒙特大学形态、进化与认知实验室）；Department of Biological Statistics and Computational Biology, Cornell University（康奈尔大学生物统计与计算生物学系）；Fluid Structure Interaction Research Group, Southampton Marine and Maritime Institute, University of Southampton（南安普顿大学南安普顿海洋与海事研究所流体结构相互作用研究组）
Designing soft robots poses considerable challenges: automated design approaches may be particularly appealing in this field, as they promise to optimize complex multi-material machines with very little or no human intervention. Evolutionary soft robotics is concerned with the application of optimization algorithms inspired by natural evolution in order to let soft robots (both morphologies and controllers) spontaneously evolve within physically-realistic simulated environments, figuring out how to satisfy a set of objectives defined by human designers. In this paper a powerful evolutionary sy…
This paper reviews various Evolutionary Approaches applied to the domain of Evolutionary Robotics with the intention of resolving difficult problems in the areas of robotic design and control. Evolutionary Robotics is a fast-growing field that has attracted substantial research attention in recent years. The paper thus collates recent findings along with some anticipated applications. The reviewed literature is organized systematically to give a categorical overview of recent developments and is presented in tabulated form for quick reference. We discuss the outstanding potentialities and chal…
University of Bridgeport（布里奇波特大学）；King Saud University（沙特国王大学）
Evolutionary robotics aims to automatically design autonomous adaptive morphological robots that can evolve to accomplish a specific task while adapting to environmental changes. Soft robotics have demonstrated the feasibility of evolutionary robotics for the synthesis of robots control and morphology. The motivation of developing evolutionary soft computing techniques to that can generate task oriented structures for morphological robots makes the domain of soft robotics worthy of serious investigation and research, and hence this article summarizes an important volume of research for a compu…
Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Vrije Universiteit Brussel（布鲁塞尔自由大学）；HU University of Applied Sciences Utrecht（乌得勒支应用科学大学）
We generalize the well-studied problem of gait learning in modular robots in two dimensions. Firstly, we address locomotion in a given target direction that goes beyond learning a typical undirected gait. Secondly, rather than studying one fixed robot morphology we consider a test suite of different modular robots. This study is based on our interest in evolutionary robot systems where both morphologies and controllers evolve. In such a system, newborn robots have to learn to control their own body that is a random combination of the bodies of the parents. We apply and compare two learning alg…
University of Pennsylvania（宾夕法尼亚大学）；CSIRO（澳大利亚联邦科学与工业研究组织）
We study the effects of injecting human-generated designs into the initial population of an evolutionary robotics experiment, where subsequent population of robots are optimised via a Genetic Algorithm and MAP-Elites. First, human participants interact via a graphical front-end to explore a directly-parameterised legged robot design space and attempt to produce robots via a combination of intuition and trial-and-error that perform well in a range of environments. Environments are generated whose corresponding high-performance robot designs range from intuitive to complex and hard to grasp. Onc…
Navy Center for Applied Research in Artificial Intelligence（海军人工智能应用研究中心）；Naval Research Laboratory（海军研究实验室）
Robotic hardware designs are becoming more complex as the variety and number of on-board sensors increase and as greater computational power is provided in ever-smaller packages on-board robots. These advances in hardware, however, do not automatically translate into better software for controlling complex robots. Evolutionary techniques hold the potential to solve many difficult problems in robotics which defy simple conventional approaches, but present many challenges as well. Numerous disciplines including artificial life, cognitive science and neural networks, rule-based systems, behavior-…
Ecole polytechnique fédérale de Lausanne (EPFL)（洛桑联邦理工学院）
Tensegrity structures are lightweight, can undergo large deformations, and have outstanding robustness capabilities. These unique properties inspired roboticists to investigate their use. However, the morphological design, control, assembly, and actuation of tensegrity robots are still difficult tasks. Moreover, the stiffness of tensegrity robots is still an underestimated design parameter. In this article, we propose to use easy to assemble, actuated tensegrity modules and body-brain co-evolution to design soft tensegrity modular robots. Moreover, we prove the importance of tensegrity robots …
Self-adaptive parameters are increasingly used in the field of Evolutionary Robotics, as they allow key evolutionary rates to vary autonomously in a context-sensitive manner throughout the optimisation process. A significant limitation to self-adaptive mutation is that rates can be set unfavourably, which hinders convergence. Rate restarts are typically employed to remedy this, but thus far have only been applied in Evolutionary Robotics for mutation-only algorithms. This paper focuses on the level at which evolutionary rate restarts are applied in population-based algorithms with more than 1 …
In recent years, Evolutionary Strategies were actively explored in robotic tasks for policy search as they provide a simpler alternative to reinforcement learning algorithms. However, this class of algorithms is often claimed to be extremely sample-inefficient. On the other hand, there is a growing interest in Differentiable Robot Simulators (DRS) as they potentially can find successful policies with only a handful of trajectories. But the resulting gradient is not always useful for the first-order optimization. In this work, we demonstrate how DRS gradient can be used in conjunction with Evol…
Okinawa Institute of Science and Technology（冲绳科学技术大学院大学）
Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy -- evolution overarching development -- is observed for most multicellular lifeforms. When designing robots however, this tenet lifts: it becomes -- however natural -- a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits…
In Evolutionary Robotics a population of solutions is evolved to optimize robots that solve a given task. However, in traditional Evolutionary Algorithms, the population of solutions tends to converge to local optima when the problem is complex or the search space is large, a problem known as premature convergence. Quality Diversity algorithms try to overcome premature convergence by introducing additional measures that reward solutions for being different while not necessarily performing better. In this paper we compare a single objective Evolutionary Algorithm with two diversity promoting se…
Istituto Italiano di Tecnologia（意大利理工学院）；São Carlos School of Engineering, University of São Paulo（圣保罗大学圣卡洛斯工程学院）
The field of physical human-robot interaction has dramatically evolved in the last decades. As a result, the robotic system's requirements have become more challenging, including personalized behavior for different tasks and users. Various machine learning techniques have been proposed to give the robot such adaptability features. This paper proposes a model-based evolutionary optimization algorithm to tune the apparent impedance of a wrist rehabilitation device. We used passivity to define boundaries for the possible controller outcomes, limiting the shared autonomy of the robot and ensuring …
This paper presents evolutionary methods for optimization in dynamic mobile robot path planning. In dynamic mobile path planning, the goal is to find an optimal feasible path from starting point to target point with various obstacles, as well as smoothness and safety in the proposed path. Pattern search (PS) algorithm, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to find an optimal path for mobile robots to reach to target point with obstacle avoidance. For showing the success of the proposed method, first they are applied to two different paths with a dynamic environm…
Finetuning Deep Reinforcement Learning Policies with Evolutionary Strategies for Control of Underactuated Robots
Department of Information Engineering, University of Padova（信息工程系，帕多瓦大学）；Human-Inspired Technology Research Center, University of Padova（人机协同技术研究中心，帕多瓦大学）
The Evaluation Cost of Task Specialization in Evolutionary Multi-Robot Systems
University of Konstanz（康斯坦茨大学）；Centre for the Advanced Study of Collective Behaviour（集体行为高级研究中心）；Department of Computer and Information Science（计算机与信息科学系）；University of Technology Nuremberg（纽伦堡工业大学）；Department of Computer Science and Artificial Intelligence（计算机科学与人工智能系）
Robotics at Google（谷歌机器人团队）；Columbia University（哥伦比亚大学）
Learning adaptable policies is crucial for robots to operate autonomously in our complex and quickly changing world. In this work, we present a new meta-learning method that allows robots to quickly adapt to changes in dynamics. In contrast to gradient-based meta-learning algorithms that rely on second-order gradient estimation, we introduce a more noise-tolerant Batch Hill-Climbing adaptation operator and combine it with meta-learning based on evolutionary strategies. Our method significantly improves adaptation to changes in dynamics in high noise settings, which are common in robotics appli…
Designing optimal soft modular robots is difficult, due to non-trivial interactions between morphology and controller. Evolutionary algorithms (EAs), combined with physical simulators, represent a valid tool to overcome this issue. In this work, we investigate algorithmic solutions to improve the Quality Diversity of co-evolved designs of Tensegrity Soft Modular Robots (TSMRs) for two robotic tasks, namely goal reaching and squeezing trough a narrow passage. To this aim, we use three different EAs, i.e., MAP-Elites and two custom algorithms: one based on Viability Evolution (ViE) and NEAT (ViE…
Xiamen University（厦门大学）；City University of Hong Kong（香港城市大学）
Robot gait optimization is the task of generating an optimal control trajectory under various internal and external constraints. Given the high dimensions of control space, this problem is particularly challenging for multi-legged robots walking in complex and unknown environments. Existing literatures often regard the gait generation as an optimization problem and solve the gait optimization from scratch for robots walking in a specific environment. However, such approaches do not consider the use of pre-acquired knowledge which can be useful in improving the quality and speed of motion gener…
Evolutionary robotics has aimed to optimize robot control and morphology to produce better and more robust robots. Most previous research only addresses optimization of control, and does this only in simulation. We have developed a four-legged mammal-inspired robot that features a self-reconfiguring morphology. In this paper, we discuss the possibilities opened up by being able to efficiently do experiments on a changing morphology in the real world. We discuss present challenges for such a platform and potential experimental designs that could unlock new discoveries. Finally, we place our rob…
