Low Complexity Control Policy Synthesis for Embodied Computation in Synthetic Cells

Ana Pervan*, Todd D. Murphey

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Scopus citations


As robots become more capable, they also become more complicated—either in terms of their physical bodies or their control architecture, or both. An iterative algorithm is introduced to compute feasible control policies that achieve a desired objective while maintaining a low level of design complexity (quantified using a measure of graph entropy) and a high level of task embodiment (evaluated by analyzing the Kullback-Leibler divergence between physical executions of the robot and those of an idealized system). When the resulting control policy is sufficiently capable, it is projected onto a set of sensor states. The result is a simple, physically-realizable design that is representative of both the control policy and the physical body. This method is demonstrated by computationally optimizing a simulated synthetic cell.

Original languageEnglish (US)
Title of host publicationSpringer Proceedings in Advanced Robotics
PublisherSpringer Science and Business Media B.V.
Number of pages17
StatePublished - 2020

Publication series

NameSpringer Proceedings in Advanced Robotics
ISSN (Print)2511-1256
ISSN (Electronic)2511-1264

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Mechanical Engineering
  • Engineering (miscellaneous)
  • Artificial Intelligence
  • Computer Science Applications
  • Applied Mathematics


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