Morphogenetic Robotics

Definition of Morphogenetic Robotics

Morphogenetic robotics was first formally defined in [1]. It generally refers to the methodologies that address challenges in robotics inspired by biological morphogenesis. Morphogenetic robotics (MR) includes, but is not limited to the following main topics:
• Morphogenetic swarm robots that deals with the self-organization of multi-robots using genetic and cellular mechanisms governing the biological early morphogenesis.
• Morphogenetic modular robots where modular robots adapt their configuration autonomously using morphogenetic principles.
• Developmental approaches to 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 or a gene regulatory network model.

Morphogenetic robotics is related to epigenetic robotics. The main difference between morphogenetic robotics and epigenetic robotics is that the former focuses on self-organization, self-reconfiguration 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 cognitive development in robotic systems, such as language, emotion and social skills, through experience during the lifetime (activity-dependent development). Research topics covered by epigenetic robotics are also termed as autonomous mental development or cognitive developmental robotics.

In biology, the term epigenetic can be derived from either epigenesis that describes morphogenesis and postnatal developmental of organisms, or from epigenetics, which refers to phenotypic changes or change in gene expression which are caused by non-genetic changes, such as DNA methylation, RNA silencing and histone modifications. To avoid confusion, developmental cognitive robotics has also been suggested. Finally, we believe that morphogenetic robotics, which is concerned with physical development of robots, and epigenetic robotics, which is responsible for mental development of robots, should as a whole lay the main foundations for developmental robotics.

Towards Evolutionary Developmental Robotics

We believe that evolutionary robotics and developmental robotics, two distinct yet complementary disciplines in robotics, should also integrate and form a new discipline: evolutionary developmental robotics , evo-devo-robo for short. Related ideas have been proposed and discussed in a dialog column articles in the Newsletter of the Autonomous Mental Development Technical Committee published in April 2011 [2][3].

References

1. Joseph Chrol-Cannon and Yaochu Jin. Learning structure of sensory inputs with synaptic plasticity leads to interference. Frontiers in Computational Neuroscience, 2015.doi: 10.3389/fncom.2015.00103
2. Joseph Chrol-Cannon and Yaochu Jin. Computational modeling of neural plasticity for self-organization of neural networks. BioSystems, 125:43-54, 2014 Also here
3. Joseph Chrol-Cannon and Yaochu Jin. On the correlation between reservoir metrics and performance for time series classification under the influence of synaptic plasticity. PLOS ONE, DOI: 10.1371/journal.pone.0101792, July 10, 2014.
4. Daniel Bush and Yaochu Jin. Calcium control of hippocampal STDP. Journal of Computational Neuroscience. 33(3):495-514, 2012
5. Joseph Chrol-Cannon, Andre Gruning and Yaochu Jin. The emergence of polychronous groups under varying input patterns, plasticity rules and network connectivities. 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, June 2012
6. 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 A PDF of preprint here
7. Y. Jin and Y. Meng. Evolutionary Developmental Robotics ?The Next Step to Go? The Newletter of the Autonomous Mental Development Technical Committee, Vol. 8, No 1, pp. 13-14, April 2011
8. Y. Jin and Y. Meng. Reply and Summary: Evolutionary Developmental Robotics ?The Next Step to Go? Newsletter of the Autonomous Mental Development Technical Committee, Vol. 8, No 2, pp. 9-11, October 2011
9. L. Schramm, Y. Jin, and B. Sendhoff. Evolution and analysis of genetic networks for stable cellular growth and regeneration. Artificial Life, 2012 (accepted)
10. D. Bush and Y. Jin. Calcium control of triphasic hippocampal STDP. Journal of Computational Neuroscience. 2012 (accepted)
11. J. Yin, Y. Meng and Y. Jin. A developmental approach to structural self-organization in reservoir computing. IEEE Transactions on Autonomous Mental Development, 2012 (in press)
12. 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. See also here for a draft.
13. 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, Volume 7 Issue 1, Article No. 15, April 2012. doi>10.1145/2168260.2168275
14. B. Inden, Y. Jin, R. Haschke, H. Ritter. Evolving neural fields for problems with large input and output spaces. Neural Networks, 28:24-39, 2012
15. 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. See also here for a preprint.
16. 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 A PDF of preprint here
17. H. Guo, Y. Meng, and Y. Jin. Swarm robot pattern formation using a morphogenetic multi-cellular based self-organization algorithm. 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China, May 9-13, 2011 (accepted)
18. Y. Meng, Y. Zhang, A. Sampath, Y. Jin, and B. Sendhoff. Cross-ball: A new morphogenetic self-reconfigurable modular robot. 2011 IEEE International Conference on Robotics and Automation (ICRA 2011), Shanghai, China, May 9-13, 2011 (accepted)
19. 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, 2009PDF here
    
20. G.S. Hornby and J.B. Pollack. Body-brain co-evolution using L-systems as a generative encoding. Artificial Life, 8:3, 2002
    
21. 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
22. M. Mamei, M. Vasirani, F. Zambonelli, Experiments in morphogenesis in swarms of simple mobile robots. Applied Artificial Intelligence, 18, 9-10: 903-919, 2004
23. M Mazzapioda, A. Cangelosi, S. Nolfi. Evolving morphology and control: A distributed approach. IEEE Congress on Evolutionary Computation. Page(s):2217 - 2224, 18-21 May 2009
24. 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
25. W. Shen, P. Will and A. Galstyan. Hormone-inspired self-organization and distributed control of robotic swarms. Autonomous Robots, 17, pp.93-105, 2004
26. T. Taylor. A genetic regulatory network-inspired real-time controller for a group of nnderwater robots. Proceedings of Eighth Conference on Intelligent Autonomous Systems (IAS-8), 2004
27. L. Wolpert. Principles of Development. Oxford University Press, 2002

Related Events

1. Workshop on Evolutionary Developmental Robotics within GECCO 2012
2. A paper on "Modeling activity-dependent plasticity in BCM spiking neural networks with application to human behavior recognition" has been accpeted to publish in IEEE Transactions on Neural Networks
3. Invited Keynote, "Morphogenetic Self-Organization of Swarm Robotic Systems for Robust Boundary Coverage and Target Tracking", The 7th International Conference on Computational Intelligence and Security, December 3-4, 2011, Sanya, Hainan, China
4. Invited Keynote, "Morphogenetic Self-Organization of Collective Systems", Organic Computing Workshop, The 8th International Conference on Autonomic Computing Karlsruhe, Germany June 14-18, 2011
5. Tutorial on "Morphogenetic Robotics: A New Emerging Field of Self-Organizing Robotic Systems", 2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai, China
6. Plenary talk, "Morphogenetic robotics", World Congress on Nature and Biologically Inspired Computing, Kitakyushu, Japan, December 15-17, 2010
7. IEEE Transactions on Autonomous Mental Development, Special Issue on Computational Modeling of Brain and Nervous Development. Submission deadline: Oct. 31, 2010.
8. Workshop on Bio-Inspired Self-Organizing Robotic Systems, within the 2010 IEEE International Conference on Robotics and Automation, Anchorage, Alaska, May 3-8, 2010
9. A Wiki article on Evolutionary developmental robotics was created by Yaochu Jin on December 1, 2009
10. Special issue on "Morphogenetic robotics", IEEE Computational Intelligence Magazine, 5(3), 2010 (Invited contributions only)
11. Special Session on Bio-Inspired Self-Organizing Multi-Agent Systems, WCCI 2010, July 18-23,2010, Barcelona, Spain
12. Special issue on Evolving Developmental Systems, IEEE Transactions on Evolutionary Computation. Submission deadline: April 30, 2010
13. A Wiki article on Morphogenetic robotics was created by Yaochu Jin on October 12, 2009
14. Ninth International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems Venice, Italy, November 12-14, 2009

---

Copyright©2009-2015. Last update in August, 2015 by Yaochu Jin. All rights reserved. The views expressed on this home page are personal and do NOT necessarily reflect the views of his employer.