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A Developmental evolutionary Learning Framework for Robotic Chinese Stroke Writing

IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2022年第3期14卷 1155-1169页

作者： Wu, Ruiqi Chao, Fei Zhou, Changle Huang, Yuxuan Yang, Longzhi Lin, Chih-Min Chang, Xiang Shen, Qiang Shang, Changjing Henan Univ Technol Sch Artificial Intelligence & Big Data Dept Artificial Intelligence Zhengzhou 471023 Peoples R China Xiamen Univ Sch Informat Dept Artificial Intelligence Xiamen 361005 Peoples R China Aberystwyth Univ Inst Math Phys & Comp Sci Dept Comp Sci Aberystwyth SY23 3DB Dyfed Wales Software Dev Ctr Bank Commun Shanghai 518028 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England Yuan Ze Univ Dept Elect Engn Taoyuan 32003 Taiwan

The ability of robots to write Chinese strokes, which is recognized as a sophisticated task, involves complicated kinematic control algorithms. The conventional approaches for robotic writing of Chinese strokes often suffer from limited font generation methods, which limits the ability of robots to perform high-quality writing. This article instead proposes a developmental evolutionary learning framework that enables a robot to learn to write fundamental Chinese strokes. The framework first considers the learning process of robotic writing as an evolutionary easy-to-difficult procedure. Then, a developmental learning mechanism called "Lift-constraint, act and saturate" that stems from developmental robotics is used to determine how the robot learns tasks ranging from simple to difficult by building on the learning results from the easy tasks. The developmental constraints, which include altitude adjustments, number of mutation points, and stroke trajectory points, determine the learning complexity of robot writing. The developmental algorithm divides the evolutionary procedure into three developmental learning stages. In each stage, the stroke trajectory points gradually increase, while the number of mutation points and adjustment altitudes gradually decrease, allowing the learning difficulties involved in these three stages to be categorized as easy, medium, and difficult. Our robot starts with an easy learning task and then gradually progresses to the medium and difficult tasks. Under various developmental constraint setups in each stage, the robot applies an evolutionary algorithm to handle the basic shapes of the Chinese strokes and eventually acquires the ability to write with good quality. The experimental results demonstrate that the proposed framework allows a calligraphic robot to gradually learn to write five fundamental Chinese strokes and also reveals a developmental pattern similar to that of humans. Compared to an evolutionary algorithm without the dev

关键词： Robots Robot kinematics Writing Trajectory Robot sensing systems evolutionary computation Task analysis Developmental learning evolutionary algorithm evolutionary robotics robotic writing

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Filling the Reality Gap: Using Obstacles to Promote Robust Gaits in evolutionary robotics

Filling the Reality Gap: Using Obstacles to Promote Robust G...

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IEEE International Conference on Evolvable Systems (ICES)

作者： Glette, Kyrre Johnsen, Andreas Leret Samuelsen, Eivind Univ Oslo Dept Informat N-0316 Oslo Norway

ISBN: (纸本)9781479944798

In evolutionary robotics, which concerns automatic design of robotic systems using evolutionary algorithms, the well-known reality gap phenomenon occurs when transferring results from simulation to real world robots. Several approaches have been proposed to tackle this challenge, such as improving the simulator, avoiding poorly simulated solutions, or promoting robust controllers by introducing noise in the simulation. In this paper we investigate if the addition of a set of small obstacles in the simulated environment can help promote more robust gaits when transferred to a real world robot. In total 80 robot gaits are tested in the real world, evolved using flat and obstacle-seeded ground planes, and using two different scenario difficulties. The results show that in the baseline scenario the proposed obstacle method has little impact on the reality gap of the evolved gaits, whereas there is a significant reduction for the difficult scenario: The average real world performance ratio is 2.3 times higher than the result obtained with the flat plane, and there are no null-performing gaits.

关键词： collision avoidance evolutionary computation mobile robots robust control automatic design evolutionary algorithms evolutionary robotics evolved gaits flat plane imulated environment null-performing gaits obstacle method obstacle-seeded ground planes real world robots reality gap phenomenon robotic systems robust controllers robust gaits simulated solutions simulator Joints Knee Legged locomotion Noise Robustness Servomotors evolutionary algorithm Computer-Aided Design Robust controllers Servo Device Component Biped locomotion Gait Collision avoidance Mobile robots Robust control simulator Noise Joints knee Reality

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Some Distance Measures for Morphological Diversification in Generative evolutionary robotics 14

Some Distance Measures for Morphological Diversification in ...

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16th Genetic and evolutionary Computation Conference (GECCO)

作者： Samuelsen, Eivind Glette, Kyrre Univ Oslo Inst Informat N-0316 Oslo Norway

ISBN: (纸本)9781450326629

evolutionary robotics often involves optimization in large, complex search spaces, requiring good population diversity. Recently, measures to actively increase diversity or novelty have been employed in order to get sufficient exploration of the search space either as the sole optimization objective or in combination with some performance measurement. When evolving morphology in addition to the control system, it can be difficult to construct a measure that sufficiently captures the qualitative differences between individuals. In this paper we investigate four diversity measures, applied in a set of evolutionary robotics experiments using an indirect encoding for evolving robot morphology. In the experiments we optimize forward locomotion capabilities of symmetrical legged robots in a physics simulation. Two distance measures in Cartesian phenotype feature spaces are compared with two methods operating in the space of possible morphology graphs. These measures are used for computing a diversity objective in a multi-objective evolutionary algorithm, and compared to a control case with no diversity objective. For the given task one of the distance measures shows a clear improvement over the control case in improving the main objectives, while others display better ability to diversify, underlining the difficulty of designing good, general measures of morphological diversity.

关键词： Design experimentation evolutionary robotics indirect encoding multiple solutions / niching

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Comparing Robot Controller Optimization Methods on Evolvable Morphologies

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evolutionary COMPUTATION 2024年第2期32卷 105-124页

作者： van Diggelen, Fuda Ferrante, Eliseo Eiben, A. E. Vrije Univ Amsterdam Dept Comp Sci Amsterdam Netherlands Technol Innovat Inst POB 9639 Abu Dhabi U Arab Emirates

In this paper, we compare Bayesian Optimization, Differential Evolution, and an Evolution Strategy employed as a gait-learning algorithm in modular robots. The motivational scenario is the joint evolution of morphologies and controllers, where "newborn" robots also undergo a learning process to optimize their inherited controllers (without changing their bodies). This context raises the question: How do gait-learning algorithms compare when applied to various morphologies that are not known in advance (and thus need to be treated as without priors)? To answer this question, we use a test suite of twenty different robot morphologies to evaluate our gait-learners and compare their efficiency, efficacy, and sensitivity to morphological differences. The results indicate that Bayesian Optimization and Differential Evolution deliver the same solution quality (walking speed for the robot) with fewer evaluations than the Evolution Strategy. Furthermore, the Evolution Strategy is more sensitive for morphological differences (its efficacy varies more between different morphologies) and is more subject to luck (repeated runs on the same morphology show greater variance in the outcomes).

关键词： evolutionary robotics morphological evolution controller optimization

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Pressure-Based Soft Agents

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ARTIFICIAL LIFE 2024年第2期30卷 240-258页

作者： Pigozzi, Federico Univ Trieste Dept Engn & Architecture Trieste Italy

Biological agents have bodies that are composed mostly of soft tissue. Researchers have resorted to soft bodies to investigate Artificial Life (ALife)-related questions;similarly, a new era of soft-bodied robots has just begun. Nevertheless, because of their infinite degrees of freedom, soft bodies pose unique challenges in terms of simulation, control, and optimization. Herein I propose a novel soft-bodied agents formalism, namely, pressure-based soft agents (PSAs): spring-mass membranes containing a pressurized medium. Pressure endows the agents with structure, while springs and masses simulate softness and allow the agents to assume a large gamut of shapes. PSAs actuate both locally, by changing the resting lengths of springs, and globally, by modulating global pressure. I optimize the controller of PSAs for a locomotion task on hilly terrain, an escape task from a cage, and an object manipulation task. The results suggest that PSAs are indeed effective at the tasks, especially those requiring a shape change. I envision PSAs as playing a role in modeling soft-bodied agents, such as soft robots and biological cells.

关键词： Embodied cognition soft robotics evolutionary robotics shape change

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Heritability of morphological and behavioural traits in evolving robots

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evolutionary INTELLIGENCE 2024年第3期17卷 1733-1749页

作者： De Carlo, Matteo Ferrante, Eliseo Zeeuwe, Daan Ellers, Jacintha Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands Autonomous Robot Res Ctr Technol Innovat Inst Abu Dhabi U Arab Emirates

In the field of evolutionary robotics, choosing the correct genetic representation is a complicated and delicate matter, especially when robots evolve behaviour and morphology at the same time. One principal problem is the lack of methods or tools to investigate and compare representations. In this paper we introduce and evaluate such a tool based on the biological notion of heritability. Heritability captures the proportion of phenotypic variation caused by genotypic variation and is often used to better understand the transmissibility of traits in real biological systems. As a proof of concept, we compare the heritability of various robot traits in two systems, one using a direct (tree based) representation and one using an indirect (grammar based) representation. We measure changes in heritability during the course of evolution and investigate how direct and indirect representation can be biased towards more exploration or exploitation throughout the course of evolution. The empirical study shows that heritability can be a useful tool to analyze different representations without running complete evolutionary processes using them.

关键词： evolutionary robotics Morphological evolution Representation Heritability

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Premature convergence in morphology and control co-evolution: a study

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ADAPTIVE BEHAVIOR 2024年第2期32卷 137-165页

作者： Eguiarte-Morett, Luis Aguilar, Wendy Univ Nacl Autonoma Mexico Posgrad Ciencia & Ingn Comp Mexico City Mexico Univ Nacl Autonoma Mexico Dept Comp Sci Inst Invest Matemat Aplicadas & Sistemas Mexico City Mexico Univ Nacl Autonoma Mexico Dept Comp Sci Inst Invest Matemat Aplicadas & Sistemas Mexico City 04510 Mexico

This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.

关键词： evolutionary robotics morphology and control co-evolution premature convergence morphological diversity comparative analysis

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Embodied Artificial Life at an Impasse Can evolutionary robotics Methods Be Scaled?

Embodied Artificial Life at an Impasse Can Evolutionary Robo...

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2014 IEEE Symposium on Evolving and Autonomous Learning Systems (EALS)

作者： Nelson, Andrew L. Androtics LLC Tucson AZ 85733 USA

ISBN: (纸本)9781479944941

evolutionary robotics (ER) investigates the application of artificial evolution toward the synthesis of robots capable of performing autonomous behaviors. Over the last 25 years, researchers have reported increasingly complex evolved behaviors, and have compiled a de facto set of benchmark tasks. Perhaps the best known of these is the obstacle avoidance and target homing task performed by differential drive robots. More complex tasks studied in recent ER work include augmented variants of the rodent T-maze and complex foraging tasks. But can proof-of-concept results such as these be extended to evolve complex autonomous behaviors in a general sense? In this topical analysis paper we survey relevant research and make the case that common tasks used to demonstrate the effectiveness of evolutionary robotics are not characteristic of more general cases and in fact do not fully prove the concept that artificial evolution can be used to evolve sophisticated autonomous agent behaviors. Robots capable of performing many of the tasks studied in ER have now been evolved using nearly aggregate binary success/fail fitness functions. However, arguments used to support the necessity of incremental methods for complex tasks are essentially sound. This raises the possibility that the tasks themselves allow for relatively simple solutions, or span a relatively small candidate solution set. This paper presents these arguments in detail and concludes with a discussion of current ER research.

关键词： evolutionary robotics artificial life artificial evolution open-ended evolution genetic algorithms

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Interacting Robots in an Artificial evolutionary Ecosystem 6th

Interacting Robots in an Artificial Evolutionary Ecosystem

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26th European Conference on Genetic Programming (EuroGP) Held as Part of EvoStar

作者： De Carlo, Matteo Ferrante, Eliseo Ellers, Jacintha Meynen, Gerben Eiben, A. E. Vrije Univ Amsterdam Boelelaan 1111 NL-1081 HV Amsterdam Netherlands Technol Innovat Inst Abu Dhabi U Arab Emirates

ISBN: (纸本)9783031295720;9783031295737

In evolutionary robotics where both body and brain are malleable, it is common practice to evaluate individuals in isolated environments. With the objective of implementing a more naturally plausible system, we designed a single interactive ecosystem for robots to be evaluated in. In this ecosystem robots are physically present and can interact each other and we implemented decentralized rules for mate selection and reproduction. To study the effects of evaluating robots in an interactive ecosystem has on evolution, we compare the evolutionary process with a more traditional, oracle-based approach. In our analysis, we observe how the different approach has a substantial impact on the final behaviour and morphology of the robots, while maintaining decent fitness performance.

关键词： evolutionary Computing evolutionary robotics Robot Interaction Artificial Ecosystem Interactive Robot Ecosystem

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The Design, Education and Evolution of a Robotic Baby

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IEEE TRANSACTIONS ON robotics 2023年第3期39卷 2488-2507页

作者： Zhu, Hanqing Wilson, Sean Feron, Eric Georgia Inst Technol Coll Comp Atlanta GA 30332 USA Georgia Inst Technol Coll Engn Atlanta GA 30332 USA Georgia Inst Technol Georgia Tech Res Inst Atlanta GA 30332 USA King Abdullah Univ Sci & Technol Div Comp Elect & Math Sci & Engn Mecca 23955 Saudi Arabia

Inspired by Alan Turing's idea of a child machine, in this article, we introduce the formal definition of a robotic baby, an integrated system with minimal world knowledge at birth, capable of learning incrementally and interactively, and adapting to the world. Within the definition, fundamental capabilities and system characteristics of the robotic baby are identified and presented as the system-level requirements. As a minimal viable prototype, the Baby architecture is proposed with a systems engineering design approach to satisfy the system-level requirements, which has been verified and validated with simulations and experiments on a robotic system. We demonstrate the capabilities of the robotic baby in natural language acquisition and semantic parsing in English and Chinese, as well as in natural language grounding, natural language reinforcement learning, natural language programming, and system introspection for explainability. The education and evolution of the robotic baby are illustrated with real-world robotic demonstrations. Inspired by the genetic inheritance in human beings, knowledge inheritance in robotic babies and its benefits regarding evolution are discussed.

关键词： Pediatrics Robots Natural languages Semantics Programming profession Robot sensing systems Context modeling evolutionary robotics explainable artificial intelligence (AI) learning and adaptive systems natural language programming robotic architecture robotic baby semantic representation

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