Soft machines display shape adaptation to external circumstances due to their intrinsic compliance. To achieve increasingly more responsive behaviors upon interactions without relying on centralized computation, embodying memory directly in the machines' structure is crucial. Here, we harness the bistability of elastic shells to alter the fluidic properties of an enclosed cavity, thereby switching between stable frequency states of a locomoting self-oscillating machine. To program these memory states upon interactions, we develop fluidic circuits surrounding the bistable shell, with soft tubes that kink and unkink when externally touched. We implement circuits for both long-term and short-term memory in a soft machine that switches behaviors in response to a human user and that autonomously changes direction after detecting a wall. By harnessing only geometry and elasticity, embodying memory allows physical structures without a central brain to exhibit autonomous feats that are typically reserved for computer-based robotic systems.

Most artificial intelligence systems can be advanced by adding more: more computing power, more lines of code, more analysis, more neural networks, more machine learning. And this is great if you have large amounts of power and space at your disposal, like on a car, or a rocket ship, or in a data center. But if you don't have all that? Then you have to get simple and think creatively. That's what Johannes Overvelde and his team at AMOLF, a government-funded Dutch physics research institute, did in a new study released this week in Proceedings of the National Academy of Sciences.