Evolving embodied intelligence from materials to machines

Natural lifeforms specialize to their environmental niches across many levels, from low-level features such as DNA and proteins, through to higher-level artefacts including eyes, limbs and overarching body plans. We propose ‘multi-level evolution’, a bottom-up automatic process that designs robots across multiple levels and niches them to tasks and environmental conditions. Multi-level evolution concurrently explores constituent molecular and material building blocks, as well as their possible assemblies into specialized morphological and sensorimotor configurations. Multi-level evolution provides a route to fully harness a recent explosion in available candidate materials and ongoing advances in rapid manufacturing processes. We outline a feasible architecture that realizes this vision, highlight the main roadblocks and how they may be overcome, and show robotic applications to which multi-level evolution is particularly suited. By forming a research agenda to stimulate discussion between researchers in related fields, we hope to inspire the pursuit of multi-level robotic design all the way from material to machine.A new vision for robot engineering, building on advances in computational materials techniques, additive and subtractive manufacturing as well as evolutionary computing, describes how to design a range of specialized robots uniquely suited to specific tasks and environmental conditions.

[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 .