Morphogenetic robotics explained

Morphogenetic robotics ^[1] generally refers to the methodologies that address challenges in robotics inspired by biological morphogenesis.^{[2] [3]}

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;^{[4] [5] [6] [7] [8] [9]}
• "Morphogenetic modular robots" are when modular robots adapt their configuration autonomously using morphogenetic principles;^{[10] [11]}
• "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.^{[13] [14] [15] [16] [17]}

See also

• Artificial life
• Cognitive robotics
• Developmental robotics
• Evolutionary robotics
• Evolutionary developmental robotics
• Modular design

External links

• A website on Morphogenetic Robotics maintained by Prof. Yaochu Jin
• EC FP7 Project: SWARM-ORGAN
• European Projects: Symbiotic Evolutionary Robot Organisms (SYMBRION)and Robotic Evolutionary Self-Programming and Self-Assembling Organisms (REPLICATOR)
• Laboratory of Intelligent Systems of Prof. Dario Floriano
• Cornell Computational Synthesis Laboratory of Prof. Hod Lipson
• Dynamic and Evolutionary Machine Organization Lab of Prof. John Pollack

Notes and 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
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
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
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
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
7. H. Hamann, H. Wörn, K. Crailsheim, T. Schmickl: Spatial macroscopic models of a bio-inspired robotic swarm algorithm. IROS 2008: 1415-1420
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
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
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
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
12. G.S. Hornby and J.B. Pollack. Body-brain co-evolution using L-systems as a generative encoding.Artificial Life, 8:3, 2002
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
14. G. Gomez and P. Eggenberger. Evolutionary synthesis of grasping through self-exploratory movements of a robotic hand. Congress on Evolutionary Computation, 2007
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
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
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