Building and Testing a General Intelligence Embodied in a Humanoid Robot
This work addresses the scientific and economic problem of creating human-like intelligence for automation, but it appears incremental as it builds on existing components without a breakthrough result.
The paper tackles the challenge of building a human-level general intelligence in a humanoid robot by developing a system that includes a physical robot, control software, a performance metric called g+, and an evolutionary algorithm to improve scores on this metric, reporting current and historical measurements of g+.
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.