Learning Visualization Policies of Augmented Reality for Human-Robot Collaboration
Keywords: Augmented Reality, Multi-robot systems, Imitation Learning
TL;DR: We present our framework, called VARIL, that for the first time introduces a learning-based Augmented Reality (AR) visualization strategy for human-multi-robot collaboration.
Abstract: In human-robot collaboration domains, augmented reality (AR) technologies have enabled people to visualize the state of robots. Current AR-based visualization policies are designed manually, which requires a lot of human efforts and domain knowledge. When too little information is visualized, human users find the AR interface not useful; when too much information is visualized, they find it difficult to process the visualized information. In this paper, we develop an intelligent AR agent that learns visualization policies (what to visualize, when, and how) from demonstrations. We created a Unity-based platform for simulating warehouse environments where human-robot teammates work on collaborative delivery tasks. We have collected a dataset that includes demonstrations of visualizing robots' current and planned behaviors. Our results from experiments with real human participants show that, compared with competitive baselines from the literature, our learned visualization strategies significantly increase the efficiency of human-robot teams in delivery tasks, while reducing the distraction level of human users.
Student First Author: yes
Supplementary Material: zip
Website: https://kishanchandan.github.io/varil.html
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