Information processing via physical soft body

By Kohei Nakajima, Helmut Hauser, Tao Li, Rolf Pfeifer. Scientific Reports, 2015

299 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/8369e62d661543f365e4a646148f37c2adb89364

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In Artificial Intelligence

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Recent Advances in Physical Reservoir Computing: A Review (2018, methodology): “Figure reproduced from (Nakajima et al., 2015), licensed under CC-BY 4.0. vortex in a magnetic material, as shown in Fig.”
Physical reservoir computing—an introductory perspective (2020, methodology): “…a silicone-based soft robotic arm inspired by an octopus can be used as a successful reservoir by taking the actuation sequence as the input and sensory reading as the reservoir state; indeed, this method exhibits high information-processing capability in some conditions [115, 117, 122, 123] (Fig.”