Information processing via physical soft body

TLDR

Soft machines are prized for their safety and resilience, yet their complex, nonlinear, memory‑laden body dynamics pose control challenges. The study aims to demonstrate that these seemingly undesirable dynamics can be harnessed for real‑time computation. By actuating a soft silicone arm, the authors emulate target nonlinear dynamical systems using the arm’s intrinsic body dynamics. Benchmark tasks reveal that the arm’s nonlinearity and memory enhance computational performance, and when compared to conventional machine‑learning methods, the system’s capabilities, limits, and practical range are quantified, indicating that soft bodies can serve as free computational resources.

Abstract

Abstract Soft machines have recently gained prominence due to their inherent softness and the resulting safety and resilience in applications. However, these machines also have disadvantages, as they respond with complex body dynamics when stimulated. These dynamics exhibit a variety of properties, including nonlinearity, memory and potentially infinitely many degrees of freedom, which are often difficult to control. Here, we demonstrate that these seemingly undesirable properties can in fact be assets that can be exploited for real-time computation. Using body dynamics generated from a soft silicone arm, we show that they can be employed to emulate desired nonlinear dynamical systems. First, by using benchmark tasks, we demonstrate that the nonlinearity and memory within the body dynamics can increase the computational performance. Second, we characterize our system’s computational capability by comparing its task performance with a standard machine learning technique and identify its range of validity and limitation. Our results suggest that soft bodies are not only impressive in their deformability and flexibility but can also be potentially used as computational resources on top and for free.