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.
Highlights
Soft machines have recently gained prominence due to their inherent softness and the resulting safety and resilience in applications
Using body dynamics generated from a soft silicone arm, we show that they can be employed to emulate desired nonlinear dynamical systems
We have systematically demonstrated that body dynamics originating from a soft silicone arm can be exploited to emulate nonlinear dynamical systems
Summary
Soft machines have recently gained prominence due to their inherent softness and the resulting safety and resilience in applications. They deliver vital applications for carrying fragile objects, for human-robot interaction, and for search and rescue in emergency situations, mostly due to their inherent softness that results in increased adaptivity and less damage during contact[3,4] Their production costs are relatively low, so they can be incorporated into a wide range of machines for various purposes[5]. Soft body dynamics exhibit a variety of properties, including nonlinearity, memory, and potentially infinitely many degrees of freedom[1,4] Their degrees of freedom are often larger than the number of actuators (i.e., underactuated systems), which leads to well-known difficulties in controlling them[6]. These examples clearly illustrate that the prerequisites to be a reservoir do not depend on the specific implementation of the system but rather on the properties of the system (i.e., input separability and fading memory)
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