Author name cluster

Jack Albertson

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers

2 author rows

AAMAS Conference 2022 Conference Paper

Augmented Reality Visualizations using Imitation Learning for Collaborative Warehouse Robots

Kishan Chandan
Jack Albertson
Shiqi Zhang

Augmented reality (AR) technologies have been applied to humanrobot collaboration (HRC) domains to enable people to visualize the state of the robots. Current AR-based visualization strategies are manually designed. This design process 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 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 developed a Unity-based platform for simulating warehouse environments where human-robot teammates work on collaborative delivery tasks. We have collected a dataset that includes 6000 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 strategy significantly increases the efficiency of human-robot teams in delivery tasks.

PDF

IROS Conference 2021 Conference Paper

Learning to Guide Human Attention on Mobile Telepresence Robots with 360° Vision

Kishan Chandan
Jack Albertson
Xiaohan Zhang 0002
Xiaoyang Zhang
Yao Liu 0001
Shiqi Zhang 0001

Mobile telepresence robots (MTRs) allow people to navigate and interact with a remote environment that is in a place other than the person’s true location. Thanks to the recent advances in 360° vision, many MTRs are now equipped with an all-degree visual perception capability. However, people’s visual field horizontally spans only about 120° of the visual field captured by the robot. To bridge this observability gap toward human-MTR shared autonomy, we have developed a framework, called GHAL360, to enable the MTR to learn a goal-oriented policy from reinforcements for guiding human attention using visual indicators. Three telepresence environments were constructed using datasets that are extracted from Matterport3D and collected from a real robot respectively. Experimental results show that GHAL360 outperformed the baselines from the literature in the efficiency of a human-MTR team completing target search tasks. A demo video is available: https://youtu.be/aGbTxCGJSDM

Details