Algorithmic Design for Embodied Intelligence in Synthetic Cells

Ana Pervan, Todd D. Murphey

Research output: Contribution to journalArticlepeer-review


In nature, biological organisms jointly evolve both their morphology and their neurological capabilities to improve their chances for survival. Consequently, task information is encoded in both their brains and their bodies. In robotics, the development of complex control and planning algorithms often bears sole responsibility for improving task performance. This dependence on centralized control can be problematic for systems with computational limitations, such as mechanical systems and robots on the microscale. In these cases, we need to be able to offload complex computation onto the physical morphology of the system. To this end, we introduce a methodology for algorithmically arranging sensing and actuation components into a robot design 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). This approach computes an idealized, unconstrained control policy that is projected onto a limited selection of sensors and actuators in a given library, resulting in intelligence that is distributed away from a central processor and instead embodied in the physical body of a robot. The method is demonstrated by computationally optimizing a simulated synthetic cell.

Original languageEnglish (US)
JournalIEEE Transactions on Automation Science and Engineering
StateAccepted/In press - 2020


  • Actuators
  • Aerospace electronics
  • Complexity theory
  • Design methodology
  • Robot sensing systems
  • Robots
  • Sensors
  • Task analysis
  • information theory.
  • morphological operations

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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