Building and Testing a General Intelligence Embodied in a Humanoid Robot

TL;DR

This paper presents a comprehensive approach to developing human-like intelligence in humanoid robots through a physical system, a control software, a novel performance metric called g+, and an evolutionary algorithm to enhance intelligence scores.

Contribution

It introduces a new integrated framework combining hardware, software, a performance metric, and evolutionary methods for advancing humanoid robot intelligence.

Findings

Initial measurements of the g+ metric demonstrate progress in robot intelligence.

The evolutionary algorithm effectively increases the g+ scores over time.

The system provides a scalable approach for developing human-like intelligence in robots.

Abstract

Machines with human-level intelligence should be able to do most economically valuable work. This aligns a major economic incentive with the scientific grand challenge of building a human-like mind. Here we describe our approach to building and testing such a system. Our approach comprises a physical humanoid robotic system; a software based control system for robots of this type; a performance metric, which we call g+, designed to be a measure of human-like intelligence in humanoid robots; and an evolutionary algorithm for incrementally increasing scores on this performance metric. We introduce and describe the current status of each of these. We report on current and historical measurements of the g+ metric on the systems described here.