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Morphogenetic robotics

Morphogenetic robotics generally refers to the methodologies that address challenges in robotics inspired by biological morphogenesis. This field overlaps with Morphogenetic Engineering that's extenuated inside Amorphous Computation.

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Background

Differences to epigenetic

Morphogenetic robotics is related to, but differs from, epigenetic robotics. The main difference between morphogenetic robotics and epigenetic robotics is that the former focuses on self-organization, self-reconfiguration, self-assembly and self-adaptive control of robots using genetic and cellular mechanisms inspired from biological early morphogenesis (activity-independent development), during which the body and controller of the organisms are developed simultaneously, whereas the latter emphasizes the development of robots' cognitive capabilities, such as language, emotion and social skills, through experience during the lifetime (activity-dependent development). Morphogenetic robotics is closely connected to developmental biology and systems biology, whilst epigenetic robotics is related to developmental cognitive neuroscience emerged from cognitive science, developmental psychology and neuroscience.

Topics

Morphogenetic robotics includes, but is not limited to the following main topics:

  • "Morphogenetic swarm robotics" deals with the self-organization of multi-robots using genetic and cellular mechanisms governing the biological early morphogenesis;456789
  • "Morphogenetic modular robots" are when modular robots adapt their configuration autonomously using morphogenetic principles;1011
  • "Developmental approaches" deals with the design of the body plan of robots, such as sensors and actuators, as well as the design of the controller, e.g., a neural controller using a generative coding 12 gene regulatory network model.1314151617

See also

References

  1. Y. Jin and Y. Meng. Morphogenetic robotics: An emerging new field in developmental robotics. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 41(2):145-160, 2011 https://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=5714230 ↩

  2. I. Salazar-Ciudad, H. Garcia-Fernandez, and R. V. Sole. Gene networks capable of pattern formation: from induction to reaction-diffusion. Journal of Theoretical Biology, 205:587-603, 2000 https://www.sciencedirect.com/science/article/pii/S0022519300920927 ↩

  3. L. Wolpert. Principles of Development. Oxford University Press, 2002 ↩

  4. H. Guo, Y. Meng, and Y. Jin. A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network. BioSystems, 98(3):193-203, 2009 https://www.ncbi.nlm.nih.gov/pubmed/19446001?dopt=Abstract ↩

  5. M. Mamei, M. Vasirani, F. Zambonelli, Experiments in morphogenesis in swarms of simple mobile robots. Applied Artificial Intelligence, 18, 9-10: 903-919, 2004 https://www.tandfonline.com/doi/abs/10.1080/08839510490509081 ↩

  6. W. Shen, P. Will and A. Galstyan. Hormone-inspired self-organization and distributed control of robotic swarms. Autonomous Robots, 17, pp.93-105, 2004 http://www.isi.edu/robots/papers/5273188-Shen.pdf ↩

  7. H. Hamann, H. Wörn, K. Crailsheim, T. Schmickl: Spatial macroscopic models of a bio-inspired robotic swarm algorithm. IROS 2008: 1415-1420 http://zool33.uni-graz.at/artlife/sites/default/files/hamannIros08.pdf ↩

  8. Y. Jin, H. Guo, and Y. Meng. A hierarchical gene regulatory network for adaptive multi-robot pattern formation. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 42(3):805-816, 2012 http://www.soft-computing.de/SMCB2012Draft.pdf ↩

  9. H. Guo, Y. Jin, and Y. Meng. A morphogenetic framework for self-organized multi-robot pattern formation and boundary coverage. ACM Transactions on Autonomous and Adaptive Systems, 7(1), Article No. 15, April 2012. doi:10.1145/2168260.2168275 https://www.researchgate.net/profile/Yaochu_Jin/publication/254008061_A_Morphogenetic_Framework_for_Self-Organized_Multirobot_Pattern_Formation_and_Boundary_Coverage/links/559fa2d508ae0e0bf6122f1e/A-Morphogenetic-Framework-for-Self-Organized-Multirobot-Pattern-Formation-and-Boundary-Coverage.pdf ↩

  10. T. Schmickl, J. Stradner, H. Hamann, and K. Crailsheim. Major Feedbacks that Support Artificial Evolution in Multi-Modular Robotics. Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems (IROS), Exploring New Horizons in Evolutionary Design of Robots Workshop, Oct. 11-15 2009, St. Louis, MO, USA, pp. 65-72 http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.156.6171&rep=rep1&type=pdf ↩

  11. Y. Meng, Y. Zheng and Y. Jin. Autonomous self-reconfiguration of modular robots by evolving a hierarchical mechnochemical model. IEEE Computational Intelligence Magazine, 6(1):43-54, 2011 https://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5692195 ↩

  12. G.S. Hornby and J.B. Pollack. Body-brain co-evolution using L-systems as a generative encoding. Artificial Life, 8:3, 2002 https://web.archive.org/web/20120310182144/http://idesign.ucsc.edu/papers/hornby_gecco01.pdf ↩

  13. J.A. Lee and J. Sitte. Morphogenetic Evolvable Hardware Controllers for Robot Walking. In: 2nd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2003), Feb. 18-20, 2003, Brisbane, Australia http://eprints.qut.edu.au/1806/ ↩

  14. G. Gomez and P. Eggenberger. Evolutionary synthesis of grasping through self-exploratory movements of a robotic hand. Congress on Evolutionary Computation, 2007 https://www.cs.york.ac.uk/rts/docs/CEC-2007/html/pdf/1098.pdf ↩

  15. L. Schramm, Y. Jin, B. Sendhoff. Emerged coupling of motor control and morphological development in evolution of multi-cellular animats. 10th European Conference on Artificial Life, Budapest, September 2009 http://epubs.surrey.ac.uk/532806/1/lschrammECAL09Final.pdf ↩

  16. Y. Meng, Y. Jin and J. Yin. Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition. IEEE Transactions on Neural Networks, 22(12):1952-1966, 2011 http://www.soft-computing.de/Submitted20110926.pdf ↩

  17. J. Yin, Y. Meng and Y. Jin. A developmental approach to structural self-organization in reservoir computing. IEEE Transactions on Autonomous Mental Development, 2012 https://www.researchgate.net/profile/Yaochu_Jin/publication/260662653_A_Developmental_Approach_to_Structural_Self-Organization_in_Reservoir_Computing/links/53f122ff0cf23733e813a228/A-Developmental-Approach-to-Structural-Self-Organization-in-Reservoir-Computing.pdf ↩