# Building and Testing a General Intelligence Embodied in a Humanoid Robot

arXiv: https://arxiv.org/abs/2307.16770 · HTML: https://mlsanity.com/paper/2307.16770/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 44 references that are in the map.
- First posted on arXiv 26 Jul 2023.

## Builds on (most cited first, 1 of 1)

- [RMPflow: A Geometric Framework for Generation of Multi-Task Motion Policies](https://mlsanity.com/paper/2007.14256/) (2020)

## Built on by (most cited first, 4 of 4)

- [Continuously Learning, Adapting, and Improving: A Dual-Process Approach to Autonomous Driving](https://mlsanity.com/paper/2405.15324/) (2024)
- [Towards Knowledge-driven Autonomous Driving](https://mlsanity.com/paper/2312.04316/) (2023)
- [LeapVAD: A Leap in Autonomous Driving via Cognitive Perception and Dual-Process Thinking](https://mlsanity.com/paper/2501.08168/) (2025)
- [Temporal-Spatial Object Relations Modeling for Vision-and-Language Navigation](https://mlsanity.com/paper/2403.15691/) (2024)

## Abstract (arXiv)

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.