Human Learning of Unknown Environments in Agile Guidance Tasks

TL;DR

This paper investigates how humans learn to navigate unknown environments in agile tasks by modeling sensory-motor interaction patterns as a graph learning process, emphasizing agent-environment dynamics.

Contribution

It introduces a novel framework using interaction patterns to analyze human learning and decision-making in unknown environments, incorporating agent-environment dynamics.

Findings

Humans efficiently learn to navigate obstacle fields over multiple trials.

Interaction patterns serve as fundamental units of memory for learning.

The framework highlights the importance of agent-environment dynamics in learning.

Abstract

Trained human pilots or operators still stand out through their efficient, robust, and versatile skills in guidance tasks such as driving agile vehicles in spatial environments or performing complex surgeries. This research studies how humans learn a task environment for agile behavior. The hypothesis is that sensory-motor primitives previously described as interaction patterns and proposed as units of behavior for organization and planning of behavior provide elements of memory structure needed to efficiently learn task environments. The paper presents a modeling and analysis framework using the interaction patterns to formulate learning as a graph learning process and apply the framework to investigate and evaluate human learning and decision-making while operating in unknown environments. This approach emphasizes the effects of agent-environment dynamics (e.g., a vehicle controlled by a human operator), which is not emphasized in existing environment learning studies. The framework is applied to study human data collected from simulated first-person guidance experiments in an obstacle field. Subjects were asked to perform multiple trials and find minimum-time routes between prespecified start and goal locations without priori knowledge of the environment.