We focus on the problem of designing an artificial agent  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 in order to do so, we should develop an understanding of human decision  for the task domain. In this work, we consider the domain of collaborative packing, and as a first step, we explore the mechanisms underlying human packing strategies. We conduct a user study in which human participants complete a series of packing tasks in a virtual environment. We analyze their packing strategies and discover that they exhibit specific spatial and temporal patterns (e.g., humans tend to place larger items into corners first). Our insight is 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 human perception of the AI agent. Ongoing work involves the development of a framework that incorporates the extracted insights to predict and manipulate human decision making towards an efficient route of low cognitive load. A follow-up study will evaluate our framework against a set of baselines featuring distinct 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.
We focus on the problem of designing an artificial agent , 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 in order to do so, we should develop an understanding of human decision making for the task domain. In this work, we consider the domain of collaborative packing, and as a first step, we explore the mechanisms underlying human packing strategies. Specifically, 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 that they exhibit specific spatial and temporal patterns (e.g., humans tend to place larger items into corners first). We expect that imbuing an artificial agent with an understanding of such a spatiotemporal structure will enable improved assistance, which will be reflected in the task performance and human perception of the artificial agent. 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. A follow-up study will evaluate our framework against a set of baselines featuring distinct 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.
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