Algorithmic Design for Embodied Intelligence in Synthetic Cells

Abstract

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. Note to Practitioners —As robotic systems approach the micrometer scale, designing them to be fully autonomous will rely less on onboard computation and more on component selection and design. In this article, we are motivated by synthetic cells—microscopic devices with limited actuation, sensing, and memory components. We apply tools from optimal control, graph theory, and information theory to develop a methodology for designing the electronic circuitry that relates actuation to sensing using memory and physically realizable transformations (e.g., simple logical operators). Results indicate that encoding task information in the physical body of a robot via a simple control policy leads to successful task performance. In future work, we plan to apply these methods to different robotic systems and to experimentally employ these designs on actual synthetic cells.