1
|
Zhao Z, Wu Q, Wang J, Zhang B, Zhong C, Zhilenkov AA. Exploring Embodied Intelligence in Soft Robotics: A Review. Biomimetics (Basel) 2024; 9:248. [PMID: 38667259 PMCID: PMC11047907 DOI: 10.3390/biomimetics9040248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/08/2024] [Accepted: 04/16/2024] [Indexed: 04/28/2024] Open
Abstract
Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics.
Collapse
Affiliation(s)
- Zikai Zhao
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.Z.)
| | - Qiuxuan Wu
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.Z.)
- Institute of Electrical Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- International Joint Research Laboratory for Autonomous Robotic Systems, Hangzhou 310018, China
- Institute of Hydrodynamics and Control Processes, Saint-Petersburg State Marine Technical University, 190121 Sankt-Petersburg, Russia;
| | - Jian Wang
- HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.Z.)
- International Joint Research Laboratory for Autonomous Robotic Systems, Hangzhou 310018, China
| | - Botao Zhang
- Institute of Electrical Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
- International Joint Research Laboratory for Autonomous Robotic Systems, Hangzhou 310018, China
| | - Chaoliang Zhong
- Institute of Electrical Engineering, School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
| | - Anton A. Zhilenkov
- Institute of Hydrodynamics and Control Processes, Saint-Petersburg State Marine Technical University, 190121 Sankt-Petersburg, Russia;
| |
Collapse
|
2
|
Eiben ÁE, Ellers J, Meynen G, Nyholm S. Robot Evolution: Ethical Concerns. Front Robot AI 2021; 8:744590. [PMID: 34805290 PMCID: PMC8603346 DOI: 10.3389/frobt.2021.744590] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 10/11/2021] [Indexed: 01/11/2023] Open
Abstract
Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.
Collapse
Affiliation(s)
- Ágoston E Eiben
- Department of Computer Science and Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Electronic Engineering, University of York, York, United Kingdom
| | - Jacintha Ellers
- Department of Ecological Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Gerben Meynen
- Department of Philosophy, Vrije Universiteit Amsterdam, Amsterdam, Netherlands.,Department of Law, Utrecht University, Utrecht, Netherlands
| | - Sven Nyholm
- Department of Philosophy and Religious Studies, Utrecht University, Utrecht, Netherlands
| |
Collapse
|
3
|
Gupta A, Savarese S, Ganguli S, Fei-Fei L. Embodied intelligence via learning and evolution. Nat Commun 2021; 12:5721. [PMID: 34615862 PMCID: PMC8494941 DOI: 10.1038/s41467-021-25874-z] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2021] [Accepted: 09/01/2021] [Indexed: 11/13/2022] Open
Abstract
The intertwined processes of learning and evolution in complex environmental niches have resulted in a remarkable diversity of morphological forms. Moreover, many aspects of animal intelligence are deeply embodied in these evolved morphologies. However, the principles governing relations between environmental complexity, evolved morphology, and the learnability of intelligent control, remain elusive, because performing large-scale in silico experiments on evolution and learning is challenging. Here, we introduce Deep Evolutionary Reinforcement Learning (DERL): a computational framework which can evolve diverse agent morphologies to learn challenging locomotion and manipulation tasks in complex environments. Leveraging DERL we demonstrate several relations between environmental complexity, morphological intelligence and the learnability of control. First, environmental complexity fosters the evolution of morphological intelligence as quantified by the ability of a morphology to facilitate the learning of novel tasks. Second, we demonstrate a morphological Baldwin effect i.e., in our simulations evolution rapidly selects morphologies that learn faster, thereby enabling behaviors learned late in the lifetime of early ancestors to be expressed early in the descendants lifetime. Third, we suggest a mechanistic basis for the above relationships through the evolution of morphologies that are more physically stable and energy efficient, and can therefore facilitate learning and control.
Collapse
Affiliation(s)
- Agrim Gupta
- Department of Computer Science, Stanford University, Stanford, CA, USA.
| | - Silvio Savarese
- Department of Computer Science, Stanford University, Stanford, CA, USA
| | - Surya Ganguli
- Department of Applied Physics, Stanford University, Stanford, CA, USA
- Wu-Tsai Neurosciences Institute, Stanford University, Stanford, CA, USA
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA
| | - Li Fei-Fei
- Department of Computer Science, Stanford University, Stanford, CA, USA.
- Stanford Institute for Human-Centered Artificial Intelligence, Stanford University, Stanford, CA, USA.
| |
Collapse
|
4
|
Lan G, van Hooft M, De Carlo M, Tomczak JM, Eiben A. Learning locomotion skills in evolvable robots. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.03.030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
|
5
|
Eiben A. Real-World Robot Evolution: Why Would it (not) Work? Front Robot AI 2021; 8:696452. [PMID: 34386525 PMCID: PMC8353392 DOI: 10.3389/frobt.2021.696452] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Accepted: 07/16/2021] [Indexed: 01/26/2023] Open
Abstract
This paper takes a critical look at the concept of real-world robot evolution discussing specific challenges for making it practicable. After a brief review of the state of the art several enablers are discussed in detail. It is noted that sample efficient evolution is one of the key prerequisites and there are various promising directions towards this in different stages of maturity, including learning as part of the evolutionary system, genotype filtering, and hybridizing real-world evolution with simulations in a new way. Furthermore, it is emphasized that an evolutionary system that works in the real world needs robots that work in the real world. Obvious as it may seem, to achieve this significant complexification of the robots and their tasks is needed compared to the current practice. Finally, the importance of not only building but also understanding evolving robot systems is emphasised, stating that in order to have the technology work we also need the science behind it.
Collapse
Affiliation(s)
- A.E. Eiben
- Department of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
6
|
Nygaard TF, Martin CP, Torresen J, Glette K, Howard D. Real-world embodied AI through a morphologically adaptive quadruped robot. NAT MACH INTELL 2021. [DOI: 10.1038/s42256-021-00320-3] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
|
7
|
Abstract
A long-term vision of evolutionary robotics is a technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. Evolutionary robotics has been widely used due to its capability of creating unique robot designs in simulation. Recent work has shown that it is possible to autonomously construct evolved designs in the physical domain; however, this brings new challenges: the autonomous manufacture and assembly process introduces new constraints that are not apparent in simulation. To tackle this, we introduce a new method for producing a repertoire of diverse but manufacturable robots. This repertoire is used to seed an evolutionary loop that subsequently evolves robot designs and controllers capable of solving a maze-navigation task. We show that compared to random initialisation, seeding with a diverse and manufacturable population speeds up convergence and on some tasks, increases performance, while maintaining manufacturability.
Collapse
|
8
|
Jelisavcic M, Glette K, Haasdijk E, Eiben AE. Lamarckian Evolution of Simulated Modular Robots. Front Robot AI 2019; 6:9. [PMID: 33501026 PMCID: PMC7805734 DOI: 10.3389/frobt.2019.00009] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 01/25/2019] [Indexed: 11/13/2022] Open
Abstract
We study evolutionary robot systems where not only the robot brains but also the robot bodies are evolvable. Such systems need to include a learning period right after ‘birth' to acquire a controller that fits the newly created body. In this paper we investigate the possibility of bootstrapping infant robot learning through employing Lamarckian inheritance of parental controllers. In our system controllers are encoded by a combination of a morphology dependent component, a Central Pattern Generator (CPG), and a morphology independent part, a Compositional Pattern Producing Network (CPPN). This makes it possible to transfer the CPPN part of controllers between different morphologies and to create a Lamarckian system. We conduct experiments with simulated modular robots whose fitness is determined by the speed of locomotion, establish the benefits of inheriting optimized parental controllers, shed light on the conditions that influence these benefits, and observe that changing the way controllers are evolved also impacts the evolved morphologies.
Collapse
Affiliation(s)
- Milan Jelisavcic
- Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Kyrre Glette
- RITMO, Department of Informatics, University of Oslo, Oslo, Norway
| | - Evert Haasdijk
- Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - A E Eiben
- Faculty of Science, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| |
Collapse
|
9
|
Howard D, Eiben AE, Kennedy DF, Mouret JB, Valencia P, Winkler D. Evolving embodied intelligence from materials to machines. NAT MACH INTELL 2019. [DOI: 10.1038/s42256-018-0009-9] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
|
10
|
De Winter G. AI personalities: clues from animal research. J EXP THEOR ARTIF IN 2018. [DOI: 10.1080/0952813x.2018.1430861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Affiliation(s)
- Gunnar De Winter
- School of Life Sciences, Genetics, Ecology, and Evolution Group, University of Nottingham, University Park, Nottingham, UK
| |
Collapse
|
11
|
Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior. Proc Natl Acad Sci U S A 2018; 115:885-890. [PMID: 29339510 DOI: 10.1073/pnas.1711089115] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Protocell models are used to investigate how cells might have first assembled on Earth. Some, like oil-in-water droplets, can be seemingly simple models, while able to exhibit complex and unpredictable behaviors. How such simple oil-in-water systems can come together to yield complex and life-like behaviors remains a key question. Herein, we illustrate how the combination of automated experimentation and image processing, physicochemical analysis, and machine learning allows significant advances to be made in understanding the driving forces behind oil-in-water droplet behaviors. Utilizing >7,000 experiments collected using an autonomous robotic platform, we illustrate how smart automation cannot only help with exploration, optimization, and discovery of new behaviors, but can also be core to developing fundamental understanding of such systems. Using this process, we were able to relate droplet formulation to behavior via predicted physical properties, and to identify and predict more occurrences of a rare collective droplet behavior, droplet swarming. Proton NMR spectroscopic and qualitative pH methods enabled us to better understand oil dissolution, chemical change, phase transitions, and droplet and aqueous phase flows, illustrating the utility of the combination of smart-automation and traditional analytical chemistry techniques. We further extended our study for the simultaneous exploration of both the oil and aqueous phases using a robotic platform. Overall, this work shows that the combination of chemistry, robotics, and artificial intelligence enables discovery, prediction, and mechanistic understanding in ways that no one approach could achieve alone.
Collapse
|
12
|
Parrilla-Gutierrez JM, Tsuda S, Grizou J, Taylor J, Henson A, Cronin L. Adaptive artificial evolution of droplet protocells in a 3D-printed fluidic chemorobotic platform with configurable environments. Nat Commun 2017; 8:1144. [PMID: 29074987 PMCID: PMC5658334 DOI: 10.1038/s41467-017-01161-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2017] [Accepted: 08/23/2017] [Indexed: 11/09/2022] Open
Abstract
Evolution via natural selection is governed by the persistence and propagation of living things in an environment. The environment is important since it enabled life to emerge, and shapes evolution today. Although evolution has been widely studied in a variety of fields from biology to computer science, still little is known about the impact of environmental changes on an artificial chemical evolving system outside of computer simulations. Here we develop a fully automated 3D-printed chemorobotic fluidic system that is able to generate and select droplet protocells in real time while changing the surroundings where they undergo artificial evolution. The system is produced using rapid prototyping and explicitly introduces programmable environments as an experimental variable. Our results show that the environment not only acts as an active selector over the genotypes, but also enhances the capacity for individual genotypes to undergo adaptation in response to environmental pressures.
Collapse
Affiliation(s)
| | - Soichiro Tsuda
- WestCHEM, School of Chemistry, The University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK
| | - Jonathan Grizou
- WestCHEM, School of Chemistry, The University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK
| | - James Taylor
- WestCHEM, School of Chemistry, The University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK
| | - Alon Henson
- WestCHEM, School of Chemistry, The University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK
| | - Leroy Cronin
- WestCHEM, School of Chemistry, The University of Glasgow, University Avenue, Glasgow, G12 8QQ, UK.
| |
Collapse
|
13
|
Jelisavcic M, de Carlo M, Hupkes E, Eustratiadis P, Orlowski J, Haasdijk E, Auerbach JE, Eiben AE. Real-World Evolution of Robot Morphologies: A Proof of Concept. ARTIFICIAL LIFE 2017; 23:206-235. [PMID: 28513201 DOI: 10.1162/artl_a_00231] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Evolutionary robotics using real hardware has been almost exclusively restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. We discuss a proof-of-concept study to demonstrate real robots that can reproduce. Following a general system plan, we implement a robotic habitat that contains all system components in the simplest possible form. We create an initial population of two robots and run a complete life cycle, resulting in a new robot, parented by the first two. Even though the individual steps are simplified to the maximum, the whole system validates the underlying concepts and provides a generic workflow for the creation of more complex incarnations. This hands-on experience provides insights and helps us elaborate on interesting research directions for future development.
Collapse
Affiliation(s)
- Milan Jelisavcic
- Vrije Universiteit Amsterdam, Amsterdam, Netherlands. E-mail: (M.J.); (M.deC.); (P.E.); (E.H.); (A.E.E.)
| | - Matteo de Carlo
- Vrije Universiteit Amsterdam, Amsterdam, Netherlands. E-mail: (M.J.); (M.deC.); (P.E.); (E.H.); (A.E.E.)
| | - Elte Hupkes
- Universiteit van Amsterdam, Amsterdam, Netherlands
| | - Panagiotis Eustratiadis
- Vrije Universiteit Amsterdam, Amsterdam, Netherlands. E-mail: (M.J.); (M.deC.); (P.E.); (E.H.); (A.E.E.)
| | | | - Evert Haasdijk
- Contact author
- Vrije Universiteit Amsterdam, Amsterdam, Netherlands. E-mail: (M.J.); (M.deC.); (P.E.); (E.H.); (A.E.E.)
| | | | - A E Eiben
- Vrije Universiteit Amsterdam, Amsterdam, Netherlands. E-mail: (M.J.); (M.deC.); (P.E.); (E.H.); (A.E.E.)
| |
Collapse
|
14
|
Faíña A, Jacobsen LT, Risi S. Automating the Incremental Evolution of Controllers for Physical Robots. ARTIFICIAL LIFE 2017; 23:142-168. [PMID: 28513203 DOI: 10.1162/artl_a_00226] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
Evolutionary robotics is challenged with some key problems that must be solved, or at least mitigated extensively, before it can fulfill some of its promises to deliver highly autonomous and adaptive robots. The reality gap and the ability to transfer phenotypes from simulation to reality constitute one such problem. Another lies in the embodiment of the evolutionary processes, which links to the first, but focuses on how evolution can act on real agents and occur independently from simulation, that is, going from being, as Eiben, Kernbach, & Haasdijk [2012, p. 261] put it, "the evolution of things, rather than just the evolution of digital objects.…" The work presented here investigates how fully autonomous evolution of robot controllers can be realized in hardware, using an industrial robot and a marker-based computer vision system. In particular, this article presents an approach to automate the reconfiguration of the test environment and shows that it is possible, for the first time, to incrementally evolve a neural robot controller for different obstacle avoidance tasks with no human intervention. Importantly, the system offers a high level of robustness and precision that could potentially open up the range of problems amenable to embodied evolution.
Collapse
Affiliation(s)
- Andrés Faíña
- Contact author
- IT University of Copenhagen, Copenhagen, Denmark. E-mail: (A.F.); (L.T.J.); (S.R.)
| | - Lars Toft Jacobsen
- IT University of Copenhagen, Copenhagen, Denmark. E-mail: (A.F.); (L.T.J.); (S.R.)
| | - Sebastian Risi
- IT University of Copenhagen, Copenhagen, Denmark. E-mail: (A.F.); (L.T.J.); (S.R.)
| |
Collapse
|
15
|
Weel B, D'Angelo M, Haasdijk E, Eiben AE. Online Gait Learning for Modular Robots with Arbitrary Shapes and Sizes. ARTIFICIAL LIFE 2017; 23:80-104. [PMID: 28140628 DOI: 10.1162/artl_a_00223] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Evolutionary robotics using real hardware is currently restricted to evolving robot controllers, but the technology for evolvable morphologies is advancing quickly. Rapid prototyping (3D printing) and automated assembly are the main enablers of robotic systems where robot offspring can be produced based on a blueprint that specifies the morphologies and the controllers of the parents. This article addresses the problem of gait learning in newborn robots whose morphology is unknown in advance. We investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. We establish that reinforcement learning does the job well and that it outperforms two alternative algorithms. The experiments also give insights into the online dynamics of gait learning and into the influence of the size, shape, and morphological complexity of the modular robots. These insights can potentially be used to predict the viability of modular robotic organisms before they are constructed.
Collapse
|
16
|
Vassanelli S, Mahmud M. Trends and Challenges in Neuroengineering: Toward "Intelligent" Neuroprostheses through Brain-"Brain Inspired Systems" Communication. Front Neurosci 2016; 10:438. [PMID: 27721741 PMCID: PMC5034009 DOI: 10.3389/fnins.2016.00438] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2016] [Accepted: 09/09/2016] [Indexed: 11/30/2022] Open
Abstract
Future technologies aiming at restoring and enhancing organs function will intimately rely on near-physiological and energy-efficient communication between living and artificial biomimetic systems. Interfacing brain-inspired devices with the real brain is at the forefront of such emerging field, with the term "neurobiohybrids" indicating all those systems where such interaction is established. We argue that achieving a "high-level" communication and functional synergy between natural and artificial neuronal networks in vivo, will allow the development of a heterogeneous world of neurobiohybrids, which will include "living robots" but will also embrace "intelligent" neuroprostheses for augmentation of brain function. The societal and economical impact of intelligent neuroprostheses is likely to be potentially strong, as they will offer novel therapeutic perspectives for a number of diseases, and going beyond classical pharmaceutical schemes. However, they will unavoidably raise fundamental ethical questions on the intermingling between man and machine and more specifically, on how deeply it should be allowed that brain processing is affected by implanted "intelligent" artificial systems. Following this perspective, we provide the reader with insights on ongoing developments and trends in the field of neurobiohybrids. We address the topic also from a "community building" perspective, showing through a quantitative bibliographic analysis, how scientists working on the engineering of brain-inspired devices and brain-machine interfaces are increasing their interactions. We foresee that such trend preludes to a formidable technological and scientific revolution in brain-machine communication and to the opening of new avenues for restoring or even augmenting brain function for therapeutic purposes.
Collapse
Affiliation(s)
- Stefano Vassanelli
- NeuroChip Laboratory, Department of Biomedical Sciences, University of PadovaPadova, Italy
| | | |
Collapse
|
17
|
Scheper KYW, Tijmons S, de Visser CC, de Croon GCHE. Behavior Trees for Evolutionary Robotics. ARTIFICIAL LIFE 2015; 22:23-48. [PMID: 26606468 DOI: 10.1162/artl_a_00192] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Evolutionary Robotics allows robots with limited sensors and processing to tackle complex tasks by means of sensory-motor coordination. In this article we show the first application of the Behavior Tree framework on a real robotic platform using the evolutionary robotics methodology. This framework is used to improve the intelligibility of the emergent robotic behavior over that of the traditional neural network formulation. As a result, the behavior is easier to comprehend and manually adapt when crossing the reality gap from simulation to reality. This functionality is shown by performing real-world flight tests with the 20-g DelFly Explorer flapping wing micro air vehicle equipped with a 4-g onboard stereo vision system. The experiments show that the DelFly can fully autonomously search for and fly through a window with only its onboard sensors and processing. The success rate of the optimized behavior in simulation is 88%, and the corresponding real-world performance is 54% after user adaptation. Although this leaves room for improvement, it is higher than the 46% success rate from a tuned user-defined controller.
Collapse
|
18
|
Prieto A, Bellas F, Trueba P, Duro R. Towards the standardization of distributed Embodied Evolution. Inf Sci (N Y) 2015. [DOI: 10.1016/j.ins.2015.03.044] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
|
19
|
Eiben AE, Smith J. From evolutionary computation to the evolution of things. Nature 2015; 521:476-82. [PMID: 26017447 DOI: 10.1038/nature14544] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2014] [Accepted: 03/24/2015] [Indexed: 01/18/2023]
Abstract
Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from the molecular to the astronomical. Today, the field is entering a new phase as evolutionary algorithms that take place in hardware are developed, opening up new avenues towards autonomous machines that can adapt to their environment. We discuss how evolutionary computation compares with natural evolution and what its benefits are relative to other computing approaches, and we introduce the emerging area of artificial evolution in physical systems.
Collapse
Affiliation(s)
- Agoston E Eiben
- VU University Amsterdam, de Boelelaan 1081a, 1081HV Amsterdam, the Netherlands
| | - Jim Smith
- University of the West of England, Bristol BS16 1QY, UK
| |
Collapse
|
20
|
Brodbeck L, Hauser S, Iida F. Morphological Evolution of Physical Robots through Model-Free Phenotype Development. PLoS One 2015; 10:e0128444. [PMID: 26091255 PMCID: PMC4474803 DOI: 10.1371/journal.pone.0128444] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 04/27/2015] [Indexed: 11/18/2022] Open
Abstract
Artificial evolution of physical systems is a stochastic optimization method in which physical machines are iteratively adapted to a target function. The key for a meaningful design optimization is the capability to build variations of physical machines through the course of the evolutionary process. The optimization in turn no longer relies on complex physics models that are prone to the reality gap, a mismatch between simulated and real-world behavior. We report model-free development and evaluation of phenotypes in the artificial evolution of physical systems, in which a mother robot autonomously designs and assembles locomotion agents. The locomotion agents are automatically placed in the testing environment and their locomotion behavior is analyzed in the real world. This feedback is used for the design of the next iteration. Through experiments with a total of 500 autonomously built locomotion agents, this article shows diversification of morphology and behavior of physical robots for the improvement of functionality with limited resources.
Collapse
Affiliation(s)
- Luzius Brodbeck
- Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
- * E-mail:
| | - Simon Hauser
- Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland
- Biorobotics Laboratory, EPFL—Ecole Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland
| | - Fumiya Iida
- Institute of Robotics and Intelligent Systems, Department of Mechanical and Process Engineering, ETH Zürich, 8092 Zürich, Switzerland
- Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, United Kingdom
| |
Collapse
|
21
|
Gutierrez JMP, Hinkley T, Taylor JW, Yanev K, Cronin L. Evolution of oil droplets in a chemorobotic platform. Nat Commun 2014; 5:5571. [PMID: 25482304 PMCID: PMC4268700 DOI: 10.1038/ncomms6571] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2014] [Accepted: 10/14/2014] [Indexed: 11/09/2022] Open
Abstract
Evolution, once the preserve of biology, has been widely emulated in software, while physically embodied systems that can evolve have been limited to electronic and robotic devices and have never been artificially implemented in populations of physically interacting chemical entities. Herein we present a liquid-handling robot built with the aim of investigating the properties of oil droplets as a function of composition via an automated evolutionary process. The robot makes the droplets by mixing four different compounds in different ratios and placing them in a Petri dish after which they are recorded using a camera and the behaviour of the droplets analysed using image recognition software to give a fitness value. In separate experiments, the fitness function discriminates based on movement, division and vibration over 21 cycles, giving successive fitness increases. Analysis and theoretical modelling of the data yields fitness landscapes analogous to the genotype-phenotype correlations found in biological evolution.
Collapse
Affiliation(s)
| | - Trevor Hinkley
- WestCHEM, School of Chemistry, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
| | - James Ward Taylor
- WestCHEM, School of Chemistry, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
| | | | - Leroy Cronin
- WestCHEM, School of Chemistry, University of Glasgow, University Avenue, Glasgow G12 8QQ, UK
| |
Collapse
|
22
|
|
23
|
Simultaneous versus incremental learning of multiple skills by modular robots. EVOLUTIONARY INTELLIGENCE 2014. [DOI: 10.1007/s12065-014-0109-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
24
|
Miller JF, Harding SL, Tufte G. Evolution-in-materio: evolving computation in materials. EVOLUTIONARY INTELLIGENCE 2014. [DOI: 10.1007/s12065-014-0106-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|