Evolving embodied intelligence from materials to machines
暂无分享,去创建一个
A. E. Eiben | Jean-Baptiste Mouret | David Howard | Philip Valencia | Dave Winkler | Danielle Frances Kennedy | Jean-Baptiste Mouret | A. Eiben | P. Valencia | David Howard | D. F. Kennedy | Dave Winkler
[1] R. A. Brooks,et al. Intelligence without Representation , 1991, Artif. Intell..
[2] D. Hoekman. Exploring QSAR Fundamentals and Applications in Chemistry and Biology, Volume 1. Hydrophobic, Electronic and Steric Constants, Volume 2 J. Am. Chem. Soc. 1995, 117, 9782 , 1996 .
[3] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[4] Jordan B. Pollack,et al. Automatic design and manufacture of robotic lifeforms , 2000, Nature.
[5] Stefano Nolfi,et al. Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines , 2000 .
[6] Gregory Piatetsky-Shapiro,et al. High-Dimensional Data Analysis: The Curses and Blessings of Dimensionality , 2000 .
[7] S. Carroll,et al. From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design , 2000 .
[8] N. Gostling,et al. From DNA to Diversity: Molecular Genetics and the Evolution of Animal Design , 2002, Heredity.
[9] Mário A. T. Figueiredo. Adaptive Sparseness for Supervised Learning , 2003, IEEE Trans. Pattern Anal. Mach. Intell..
[10] A. E. Eiben,et al. Introduction to Evolutionary Computing , 2003, Natural Computing Series.
[11] Bernhard Sendhoff,et al. Structure optimization of neural networks for evolutionary design optimization , 2005, Soft Comput..
[12] Robin R. Murphy,et al. How UGVs physically fail in the field , 2005, IEEE Transactions on Robotics.
[13] Rolf Pfeifer,et al. How the body shapes the way we think - a new view on intelligence , 2006 .
[14] Amanda Clare,et al. An ontology for a Robot Scientist , 2006, ISMB.
[15] Hod Lipson,et al. Resilient Machines Through Continuous Self-Modeling , 2006, Science.
[16] Javier Ruiz-del-Solar,et al. Combining Simulation and Reality in Evolutionary Robotics , 2007, J. Intell. Robotic Syst..
[17] W. Maier,et al. Combinatorial and high-throughput materials science. , 2007, Angewandte Chemie.
[18] Kenneth O. Stanley,et al. Compositional Pattern Producing Networks : A Novel Abstraction of Development , 2007 .
[19] Sung-Weon Yeom,et al. A biomimetic jellyfish robot based on ionic polymer metal composite actuators , 2009 .
[20] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[21] Yaochu Jin,et al. Surrogate-assisted evolutionary computation: Recent advances and future challenges , 2011, Swarm Evol. Comput..
[22] Jason D. Lohn,et al. Computer-Automated Evolution of an X-Band Antenna for NASA's Space Technology 5 Mission , 2011, Evolutionary Computation.
[23] Thomas Bäck,et al. Evolutionary strategies for identification and validation of material model parameters for forming simulations , 2011, GECCO '11.
[24] Serge Kernbach,et al. Embodied artificial evolution , 2012, Evolutionary Intelligence.
[25] Herschel Rabitz,et al. Control in the Sciences Over Vast Length and Time Scales , 2012 .
[26] Helmut Hauser,et al. Towards a theoretical foundation for morphological computation with compliant bodies , 2011, Biological Cybernetics.
[27] Marco Buongiorno Nardelli,et al. The high-throughput highway to computational materials design. , 2013, Nature materials.
[28] Aaron Cecala,et al. Beyond The Brain: How Body and Environment Shape Animal and Human Minds , 2013 .
[29] Stéphane Doncieux,et al. The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics , 2013, IEEE Transactions on Evolutionary Computation.
[30] Maumita Bhattacharya,et al. Evolutionary Approaches to Expensive Optimisation , 2013, ArXiv.
[31] Kristin A. Persson,et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .
[32] Hod Lipson,et al. Unshackling evolution: evolving soft robots with multiple materials and a powerful generative encoding , 2013, GECCO '13.
[33] A. Eiben,et al. The triangle of life: evolving robots in real-time and real-space The Triangle of Life: Evolving Robots in Real-time and Real-space , 2013 .
[34] Mark Hoogendoorn,et al. The Triangle of Life , 2013, ECAL.
[35] Robert J. Wood,et al. A Resilient, Untethered Soft Robot , 2014 .
[36] C. Keplinger,et al. 25th Anniversary Article: A Soft Future: From Robots and Sensor Skin to Energy Harvesters , 2013, Advanced materials.
[37] M. Míguez-Burbano,et al. Beyond the Brain , 2014, Journal of the International Association of Providers of AIDS Care.
[38] Joshua Evan Auerbach,et al. Environmental Influence on the Evolution of Morphological Complexity in Machines , 2014, PLoS Comput. Biol..
[39] LipsonHod,et al. Challenges and Opportunities for Design, Simulation, and Fabrication of Soft Robots , 2014 .
[40] Josh C. Bongard,et al. Combining Computational and Social Effort for Collaborative Problem Solving , 2015, PloS one.
[41] Antoine Cully,et al. Robots that can adapt like animals , 2014, Nature.
[42] A. E. Eiben,et al. From evolutionary computation to the evolution of things , 2015, Nature.
[43] D. Rus,et al. Design, fabrication and control of soft robots , 2015, Nature.
[44] Jean-Baptiste Mouret,et al. Illuminating search spaces by mapping elites , 2015, ArXiv.
[45] F. Iida,et al. Morphological Evolution of Physical Robots through Model-Free Phenotype Development , 2015, PloS one.
[46] Alán Aspuru-Guzik,et al. What Is High-Throughput Virtual Screening? A Perspective from Organic Materials Discovery , 2015 .
[47] A. E. Eiben,et al. Evolutionary Robotics: What, Why, and Where to , 2015, Front. Robot. AI.
[48] Mariangela Manti,et al. Stiffening in Soft Robotics: A Review of the State of the Art , 2016, IEEE Robotics & Automation Magazine.
[49] Anders Lyhne Christensen,et al. Open Issues in Evolutionary Robotics , 2016, Evolutionary Computation.
[50] Robert J. Wood,et al. An integrated design and fabrication strategy for entirely soft, autonomous robots , 2016, Nature.
[51] D. Winkler,et al. Discovery and Optimization of Materials Using Evolutionary Approaches. , 2016, Chemical reviews.
[52] Leroy Cronin,et al. Towards dial-a-molecule by integrating continuous flow, analytics and self-optimisation. , 2016, Chemical Society reviews.
[53] Kenneth O. Stanley,et al. Quality Diversity: A New Frontier for Evolutionary Computation , 2016, Front. Robot. AI.
[54] C BongardJosh,et al. Evolving Soft Robots , 2016 .
[55] Jean-Baptiste Mouret,et al. Introduction to the Evolution of Physical Systems Special Issue , 2017, Artificial Life.
[56] Su‐Ting Han,et al. An Overview of the Development of Flexible Sensors , 2017, Advanced materials.
[57] Hod Lipson,et al. Soft material for soft actuators , 2017, Nature Communications.
[58] David Howard,et al. A Platform That Directly Evolves Multirotor Controllers , 2017, IEEE Transactions on Evolutionary Computation.
[59] Navinda Kottege,et al. A testbed that evolves hexapod controllers in hardware , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[60] Xi Chen,et al. Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.
[61] Guang-Zhong Yang,et al. New materials for next-generation robots , 2017, Science Robotics.
[62] K. Shea,et al. Integrated Design and Simulation of Tunable, Multi-State Structures Fabricated Monolithically with Multi-Material 3D Printing , 2017, Scientific Reports.
[63] David A Winkler,et al. Performance of Deep and Shallow Neural Networks, the Universal Approximation Theorem, Activity Cliffs, and QSAR , 2017, Molecular informatics.
[64] Nikolaus Correll,et al. Will robots be bodies with brains or brains with bodies? , 2017, Science Robotics.
[65] Jean-Baptiste Mouret,et al. Black-box data-efficient policy search for robotics , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[66] Eujin Pei,et al. A study of 4D printing and functionally graded additive manufacturing , 2017 .
[67] Eleonora Atzeni,et al. Overview on Additive Manufacturing Technologies , 2017, Proceedings of the IEEE.
[68] Fumiya Iida,et al. The trade-off between morphology and control in the co-optimized design of robots , 2017, PloS one.
[69] Jean-Baptiste Mouret,et al. Using Parameterized Black-Box Priors to Scale Up Model-Based Policy Search for Robotics , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).
[70] Guang-Zhong Yang,et al. New materials for next-generation robots , 2017, Science Robotics.
[71] Jean-Baptiste Mouret,et al. Using Centroidal Voronoi Tessellations to Scale Up the Multidimensional Archive of Phenotypic Elites Algorithm , 2016, IEEE Transactions on Evolutionary Computation.
[72] J. Clune,et al. The Surprising Creativity of Digital Evolution , 2018, ALIFE.
[73] Yiannis Demiris,et al. Hierarchical behavioral repertoires with unsupervised descriptors , 2018, GECCO.
[74] Leroy Cronin,et al. Controlling an organic synthesis robot with machine learning to search for new reactivity , 2018, Nature.
[75] Josh C. Bongard,et al. Interoceptive robustness through environment-mediated morphological development , 2018, GECCO.
[76] Sylvain Lefebvre,et al. Polyhedral voronoi diagrams for additive manufacturing , 2018, ACM Trans. Graph..
[77] Dmitry Berenson,et al. What Happened at the DARPA Robotics Challenge Finals , 2018 .
[78] Risto Miikkulainen,et al. The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities , 2018, Artificial Life.
[79] Lin Li,et al. A novel 6-axis hybrid additive-subtractive manufacturing process: Design and case studies , 2018, Journal of Manufacturing Processes.
[80] Jean-Baptiste Mouret,et al. Data-Efficient Design Exploration through Surrogate-Assisted Illumination , 2018, Evolutionary Computation.
[81] Michael C. McAlpine,et al. 3D Printed Electrically-Driven Soft Actuators. , 2018, Extreme Mechanics Letters.
[82] Risto Miikkulainen,et al. Designing neural networks through neuroevolution , 2019, Nat. Mach. Intell..
[83] C. R. Rorem. WHAT, WHY, AND WHERE IS IT? , 1935 .