Multifunctional physical reservoir computing in soft tensegrity robots

TL;DR

This paper explores how soft tensegrity robots can utilize their nonlinear dynamics as physical reservoirs to control multiple behaviors, revealing intrinsic attractors and advancing embodied cognition understanding.

Contribution

It extends physical reservoir computing to multi-behavior control in soft robots and uncovers untrained attractors reflecting the system's intrinsic properties.

Findings

Multistable dynamical system with multiple attractors.

Existence of untrained attractors outside training data.

Potential insights into embodied cognition features.

Abstract

Recent studies have demonstrated that the dynamics of physical systems can be utilized for the desired information processing under the framework of physical reservoir computing (PRC). Robots with soft bodies are examples of such physical systems, and their nonlinear body-environment dynamics can be used to compute and generate the motor signals necessary for the control of their own behavior. In this simulation study, we extend this approach to control and embed not only one but also multiple behaviors into a type of soft robot called a tensegrity robot. The resulting system, consisting of the robot and the environment, is a multistable dynamical system that converges to different attractors from varying initial conditions. Furthermore, attractor analysis reveals that there exist "untrained attractors" in the state space of the system outside the training data. These untrained attractors reflect the intrinsic properties and structures of the tensegrity robot and its interactions with the environment. The impacts of these recent findings in PRC remain unexplored in embodied AI research. We here illustrate their potential to understand various features of embodied cognition that have not been fully addressed to date.