Towards Effective Human-AI Teams: The Case of Collaborative Packing

TL;DR

This paper investigates human packing strategies to inform the design of AI agents that assist humans in packing tasks, aiming to improve performance while reducing cognitive load.

Contribution

It provides the first analysis of human packing behaviors and proposes a framework to incorporate these insights into AI assistance strategies.

Findings

Humans tend to place larger items at corners first.

Identified spatial and temporal patterns in human packing strategies.

Insights will inform AI to improve assistance and user experience.

Abstract

We focus on the problem of designing an artificial agent (AI), capable of assisting a human user to complete a task. Our goal is to guide human users towards optimal task performance while keeping their cognitive load as low as possible. Our insight is that doing so requires an understanding of human decision making for the task domain at hand. In this work, we consider the domain of collaborative packing, in which an AI agent provides placement recommendations to a human user. As a first step, we explore the mechanisms underlying human packing strategies. We conducted a user study in which 100 human participants completed a series of packing tasks in a virtual environment. We analyzed their packing strategies and discovered spatial and temporal patterns, such as that humans tend to place larger items at corners first. We expect that imbuing an artificial agent with an understanding of this spatiotemporal structure will enable improved assistance, which will be reflected in the task performance and the human perception of the AI. Ongoing work involves the development of a framework that incorporates the extracted insights to predict and manipulate human decision making towards an efficient trajectory of low cognitive load and high efficiency. A follow-up study will evaluate our framework against a set of baselines featuring alternative strategies of assistance. Our eventual goal is the deployment and evaluation of our framework on an autonomous robotic manipulator, actively assisting users on a packing task.