No-brainer: Morphological Computation driven Adaptive Behavior in Soft Robots

TL;DR

This paper demonstrates that soft robots can achieve adaptive and complex behaviors through morphological computation alone, without relying on traditional neural network-based control systems.

Contribution

It introduces the concept of closed-loop morphological computation in soft robots, showing how simple reactive materials can produce intelligent behaviors.

Findings

Soft robots can mimic logic gates through shape-changing materials.

Morphological computation enables complex behaviors without explicit control systems.

The approach demonstrates potential for collective behaviors in soft robotics.

Abstract

It is prevalent in contemporary AI and robotics to separately postulate a brain modeled by neural networks and employ it to learn intelligent and adaptive behavior. While this method has worked very well for many types of tasks, it isn't the only type of intelligence that exists in nature. In this work, we study the ways in which intelligent behavior can be created without a separate and explicit brain for robot control, but rather solely as a result of the computation occurring within the physical body of a robot. Specifically, we show that adaptive and complex behavior can be created in voxel-based virtual soft robots by using simple reactive materials that actively change the shape of the robot, and thus its behavior, under different environmental cues. We demonstrate a proof of concept for the idea of closed-loop morphological computation, and show that in our implementation, it enables behavior mimicking logic gates, enabling us to demonstrate how such behaviors may be combined to build up more complex collective behaviors.