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Robotics-based scientific methodologies carry substantial warrant from decades of success within biorobotics (1). Increasingly clever robot designs provide platforms for both testing neuromechanical hypotheses as well as demonstrating progress toward emulating animal-like agility and dexterity. Robots as physical models for biological organisms, however, only scratch the surface of the vast potential for robotics to serve a broader and more revolutionary scientific purpose.
Over the past decade, a breadth of scientific insight has materialized that is attributable to integrating robots into experimental science outside traditional areas of biorobotics or purpose-driven robot design. This nascent field of “robophysics” has led to a predictive theory of terradynamics within complex rheological substrates (2), a unifying geometric dynamics of self-propulsion (3), and an entirely new experimental behavioral biology of eusocial and collective animals (4). Even limited and relatively unsophisticated robotic interactions have been revealing emergent properties while simultaneously challenging theorists to realistically account for sources of noise, uncertainty, and variability that have yet to be modeled.
In addition to robophysical interaction, integrating artificial intelligence (AI) into experimental design has led to systems capable of autonomously shaping experimental discovery. Salient advances have stemmed from the University of Glasgow, where evolutionary algorithms and machine learning have been used to evolve functional molecule synthesis while also uncovering relationships between nonlinear chemical environments and the collective behavior of biological protocells (5, 6 …
