This work proposes an "embryomorphic engineering" approach inspired by evo-devo to solve the paradoxical challenge of planning autonomous systems. Its goal is to artificially reconstruct complex morphogenesis by integrating three fundamental ingredients: self-assembly (SA) and pattern formation (PF) under genetic regulation (GR).

It presents a spatial computational agent-based model that can be equivalently construed as (a) moving cellular automata, in which cell rearrangement is influenced by the pattern they form, or (b) heterogeneous swarm motion, in which agents differentiate into patterns according to their location. It offers a new abstract framework to explore the causal and programmable link from genotype to phenotype that is needed in many emerging computational domains, such as "amorphous computing" or "artificial embryogeny".

Local morphogen gradients (X, Y) provide positional information in input, which is integrated by each GRN to produce differential expression of identity genes (I, J, ...) in output. Similarly to striping in the Drosophila embryo, the lattice becomes segmented into spatial domains of homogeneous genetic expression (one for each identity gene; Carroll et al. 2001) that resemble stained glass motifs (Fig. d).

Meanwhile, it also expands by cell proliferation, creating new local gradients of positional information within former single-identity domains (Fig. d, right). Analogous to a growing canvas that paints itself (Coen 2000), the alternation of growth and patterning results in the creation of a form.

Various research works have investigated the possibility of obtaining self-formation capabilities from a variety of complex computing systems. Since functionality is distributed over a great number of components, it would be an insurmountable task to assemble and instruct each of them individually. Rather, in a way similar to biological cells, these components should be easily mass-produced, initially as identical copies of each other, and only acquire their specialized positions and functions by themselves within the system, once mixed together.

For example, MIT's "amorphous computing" has set the stage for a myriad of micro-processors containing the same instructions to self-organize without exact blueprint map or functional reliability, unlike traditional VLSI (e.g., Abelson et al. 1999, Nagpal 2002). Such self-assembling components can also represent mobile sensors and actuators in complex self-managing networks (Beal & Bachrach 2006). In software applications (servers, security, etc.), a swarm of small-footprint software agents can diversify and self-deploy to achieve a desired level of service (Hofmeyr & Forrest 2000). In robotics, too, whether articulated parts of reconfigurable devices (Lipson & Pollack 2000), or mobile formations of mini-robots (Christensen et al. 2007), there is also great demand for controllable complex morphologies.