Combating Spatial Disorientation in a Dynamic Self-Stabilization Task Using AI Assistants

TL;DR

This study demonstrates that AI assistants, especially those trained with reinforcement learning, can improve human performance in spatial orientation tasks, though human trust varies based on the AI's training method.

Contribution

The paper introduces AI assistant models trained on human data to aid in spatial disorientation tasks, comparing reinforcement learning and deep learning approaches.

Findings

Reinforcement learning assistants outperform others in task metrics.

AI assistants can enhance human performance in spatial orientation tasks.

Human trust varies with AI training methodology.

Abstract

Spatial disorientation is a leading cause of fatal aircraft accidents. This paper explores the potential of AI agents to aid pilots in maintaining balance and preventing unrecoverable losses of control by offering cues and corrective measures that ameliorate spatial disorientation. A multi-axis rotation system (MARS) was used to gather data from human subjects self-balancing in a spaceflight analog condition. We trained models over this data to create "digital twins" that exemplified performance characteristics of humans with different proficiency levels. We then trained various reinforcement learning and deep learning models to offer corrective cues if loss of control is predicted. Digital twins and assistant models then co-performed a virtual inverted pendulum (VIP) programmed with identical physics. From these simulations, we picked the 5 best-performing assistants based on task metrics such as crash frequency and mean distance from the direction of balance. These were used in a co-performance study with 20 new human subjects performing a version of the VIP task with degraded spatial information. We show that certain AI assistants were able to improve human performance and that reinforcement-learning based assistants were objectively more effective but rated as less trusted and preferable by humans.