top of page

Projects

AI Teacher 

AI Teacher is a human-in-the-loop explainable AI (XAI) framework for explaining robot behavior. It includes a policy summarization algorithm based on Bayesian user modeling, an interactive user interface allowing users to ask user-specific questions, and a detailed user study showing the effect of interactive teaching techniques for XAI. 
 

Related Papers:

  • AI Teacher 1.0:  Evaluating the Role of Interactivity on Improving Transparency in Autonomous Agents (AAMAS '22) [pdf]

  • AI Teacher 2.0: Interactively Explaining Robot Policies to Humans in Integrated Virtual and Physical Training Environments (HRI'24 LBR) [pdf] [poster]

Related Video Demos:

Robot-assisted Nursing / Robot Tutor

Robot Tutor is an Intelligent Robot Tutoring system developed to train and assess novice nurses on medical procedures. The system includes planning, perception, and speech capabilities. 

Related Papers:

  • Robotic Tutors for Nurse Training: Opportunities for HRI Researchers (RO-MAN'23) [pdf]

In the demo above, Stretch performs simple routine tasks in a hospital room such as fetching and placing objects, and pushing the bedhead button to adjust the hospital bedhead. The geometric shapes can be replaced with hospital-related objects such as blood samples, masks, gloves, and tool kits.

I implemented Bayesian Knowledge Tracing (BKT) to evaluate a nurse's skills for maintaining sterilized (in this example, the hands need to be always above the waistline). The original BKT is not designed for continuous assessment so I modified it based on this paper, essentially by running BKT every 0.1 seconds. Two versions are implemented:

 

1. BKT w/o learning assumes the user is not gaining new knowledge during this assessment, thus using the initial prior for every step of the assessment;

2. BKT w/ learning assumes the user continues to learn during the assessment, thus using an updated prior.

* The vision system (left side of the screen) is built by my summer intern student Filip Bajraktari

I-CEE

One of the biggest challenges faced by AI researchers is the lack of interpretability of deep neural networks - the machine learning model worked, but why? or the machine learning model made an error, why? To address this problem, my co-authors and I propose a framework, titled I-CEE, that utilizes human cognition modeling to help human users make sense of the machine learning model. This interdisciplinary work marks novel contributions toward generating personalized explanations for users. 

Related Papers:

  • I-CEE: Tailoring Explanations of Image Classifications Models to User Expertise (AAAI'24) [pdf]

  • Literature Review: Towards Human-centered Explainable AI: User Studies for Model Explanations (IEEE TPAMI) [pdf]

bottom of page