Michael S. Lee

Hi, I am a 5th-year PhD student in the Robotics Institute at Carnegie Mellon University, working at the intersection of explainable AI and human-robot interaction. I am fortunate to be advised by Reid Simmons and Henny Admoni, and am part of the Human and Robot Partners (HARP) Lab.

To collaborate and co-exist fluently with robots, humans must be able to understand their decision making. Thus, I research how a robot may intuitively summarize and convey its reward function (and subsequent policy) to a human using informative demonstrations.

I previously completed a Master's at Carnegie Mellon with Red Whittaker and Nathan Michael studying autonomous radiation source localization. And before that, I graduated from Princeton University ('16) with degrees in Mechanical & Aerospace Engineering and a certificate in Robotics & Intelligent Systems. My undergraduate thesis on modeling uncertainty in stereo visual odometry was advised by Nathan Michael and Jianxiong Xiao.

I plan to transition into industry in Fall 2023, and am currently looking for opportunities related to my research interests (including but not limited to value/AI alignment, explainable AI, human-robot interaction, etc).

Email  /  CV  /  Google Scholar  /  LinkedIn

Toward human understanding of robot decision making, my current research explores how a robot may teach its current reward function to a human learner using informative demonstrations (i.e. ones that exhibit key tradeoffs).

We calculate the informativeness of a demonstration and a maintain a model of a human's beliefs by assuming that humans employ a form of inverse reinforcement learning.

With the key insight that informativeness and difficulty of comprehension are often two sides of the same coin, we simultaneously leverage ideas from cognitive science (e.g. zone of proximal development) to ensure that the selected demonstrations present the right level of challenge.

Finally, we are currently exploring how to select a suite of informative tests that reveal and bridge remaining gaps in the human learner's understanding, which can then be further supported through subsequent targeted demonstrations in a closed-loop fashion.

An informative demonstration is one that differs strongly from the learner’s expectations of what the robot will do given their current understanding of the robot’s decision making.

A behavior tree hierarchically composed of goals, subgoals, steps, and actions supports explanation generation algorithms that convey causal information about robot behavior.

Augmenting an inverse reinforcement learning model of how humans learn from demonstrations with teaching strategies (e.g. scaffolding, simplicity, pattern discovery, and testing) better accommodates human learners.

This proposal thus investigates how a robot can actively assess both its proficiency at a task and its confidence in that assessment through appropriate measures of uncertainty that can be efficiently and effectively communicated to a human.

I previously developed a novel gamma radiation map representation, source localization algorithm, and frontier-based exploration for efficient radiological characterization of nuclear facilities using a robot equipped with a gamma-ray camera.

The proposed frontier-based exploration method biases frontier selection with the observed radiation field gradient to quickly search an environment until a proximal source is detected.

The proposed active source localization algorithm greedily selects new waypoints that maximize the Fisher Information provided by the gamma-ray camera’s range and bearing observations.

A ground robot equipped with a Compton gamma camera localizes multiple gamma radiation sources to within an average of 0.26 m or 0.6% of the environment dimensions in two 5×4 m² and 14×6 m² laboratory environments.

Design by Jon Barron.