Interoceptive robustness through environment-mediated morphological development
This addresses the problem of robustness in AI and robotics for researchers and practitioners, though it is incremental as it builds on existing evolutionary and developmental approaches.
The study tackled the brittleness of AI and robotic systems by evolving soft robots with geometry, control, and material properties that develop in response to engineering stress during their lifetimes, resulting in robots that were equally fit but more robust to extreme material defects compared to non-developing or differently stimulated robots.
Typically, AI researchers and roboticists try to realize intelligent behavior in machines by tuning parameters of a predefined structure (body plan and/or neural network architecture) using evolutionary or learning algorithms. Another but not unrelated longstanding property of these systems is their brittleness to slight aberrations, as highlighted by the growing deep learning literature on adversarial examples. Here we show robustness can be achieved by evolving the geometry of soft robots, their control systems, and how their material properties develop in response to one particular interoceptive stimulus (engineering stress) during their lifetimes. By doing so we realized robots that were equally fit but more robust to extreme material defects (such as might occur during fabrication or by damage thereafter) than robots that did not develop during their lifetimes, or developed in response to a different interoceptive stimulus (pressure). This suggests that the interplay between changes in the containing systems of agents (body plan and/or neural architecture) at different temporal scales (evolutionary and developmental) along different modalities (geometry, material properties, synaptic weights) and in response to different signals (interoceptive and external perception) all dictate those agents' abilities to evolve or learn capable and robust strategies.