Biological Robots: Perspectives on an Emerging Interdisciplinary Field
Many fields of science and technology, as they mature, go beyond the specific matter of their early applications. Engineering is no longer limited to the study of engines. Just as the deep principles of physics and thermodynamics apply across scales, from galaxies to the soft matter of cellular processes, biology is moving from zoology to unraveling the fundamental laws of life across natural, synthetic, and exobiological systems. Computer science and robotics can lead the way, with their early emphasis on functionality and realizability, not material implementation.
Thus, importing the ideas of programmability and substrate independence will open further opportunities in the life sciences and bioengineering, to go beyond the contingent natural products of evolution to truly understand life in diverse configurations. Together, the emerging consilience of techniques and concepts at the intersection of materials science, computing, and bioengineering is an exciting new field with the potential to create technological machines that embody biological principles, and incorporate biological components, in entirely new ways.
Douglas Blackiston has a BSc in Biological Sciences from McDaniel College and a PhD in Biology from Georgetown University. His research examines the relationship between organism-level behaviors and developmental events, including the ability of memory to persist through metamorphosis in holometabolous insects and the sensory–motor integration of transplanted eyes in vertebrate models. His interest in biological machines and robots arises from this background, applying developmental and behavioral techniques from model organisms to engineer multicellular living systems with behaviors designed for specific purposes.
Sam Kriegman is an assistant professor of computer science, chemical and biological engineering, and mechanical engineering at Northwestern University. His research draws inspiration from the origin and evolution of life and applies the underlying mechanisms of self-organization and natural selection to the creation of novel autonomous machines. These machines can in some cases perform useful work, or they may be used as scientific tools to understand how animals evolve, grow, move, sense, and think.
Josh Bongard is the Cyril G. Veinott professor of computer science at the University of Vermont. His laboratory is dedicated to understanding how embodied cognition manifests in living systems, and how such discoveries can open new pathways to creating intelligent, safe, and understandable algorithms and machines. Such technologies can then, in turn, suggest new hypotheses about how organisms continuously transform themselves to track and exploit opportunities presented by their internal and external environments.
Michael Levin is distinguished professor of Biology at Tufts University and an associate faculty at Harvard's Wyss Institute for Biologically Inspired Engineering. He has dual undergraduate degrees in computer science and biology and a PhD in Genetics from Harvard Medical School. He has a long-time interest in artificial life and emergent morphogenetic and behavioral capacities in natural and artificial multiscale agential systems. His laboratory uses developmental biophysics, computational modeling, and behavioral science to understand morphogenesis and develop applications across birth defects, regenerative medicine, and cancer.
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M.L. and J.B. are scientific co-founders of a company, Fauna, which operates in the field of AI-designed biological robots. The remaining authors have no competing financial interests.
This work was sponsored by the Defense Advanced Research Projects Agency (DARPA) under Cooperative Agreement Number HR0011-18-2-0022, the Lifelong Learning Machines program from DARPA/MTO. We gratefully acknowledge support by the Allen Discovery Center program through the Paul G. Allen Frontiers Group (12171), and the National Science Foundation's Emergent Behaviors of Integrated Cellular Systems Grant (Subaward CBET-0939511). This research was also supported by the National Science Foundation's Emerging Frontiers in Research and Innovation (EFRI) Continuum, Compliant, and Configurable Soft Robotics Engineering (C3 SoRo) program (Subaward EFMA-1830870).