Bhairav Mehta

I am a research intern Master's student at Université de Montréal / Mila under the supervision of Liam Paull and Christopher Pal, with a focus on reinforcement learning. I completed my undergraduate degree at the University of Michigan where I studied Computer Science and Applied Math. At Michigan, my research projects under Satinder Singh and Matthew Johnson-Roberson focused on hierarchical RL as well as robotic reinforcement learning. I spent a semester at the Jet Propulsion Laboratory, working full-time under Larry H. Matthies in the Computer Vision Group, and spent my last semester helping teach the Introduction to Machine Learning course at Michigan with Sindhu Kutty. I now help teach Duckietown at UdeM, and will be teaching an online class on Stein's Method in Machine Learning as an inaugural Depth First Learning Fellow.

My research interests are concentrated at the intersection of robotics and reinforcement learning, including the simulation-transfer problem, but I am also try to dabble in curriculum learning, lifelong learning, and deep learning optimization. I work heavily with my advisers, as well as other folks around Mila & the Montréal AI scene. I have started to take interns with strong interest in reinforcement learning, robotics, and NLP: if that sounds like you (and you'd like to visit Mila), please fill out this form.

In the past, I had cofounded an edtech startup named Project Chronicle. We were funded by the University of Michigan CSE Department and the 1517 Fund. I am now the lead PI on a Mila project (under the umbrella of their AI for Social Good initiative) that uses machine learning to tackle some of the same problems.

I am grateful for support from Mila, Université de Montréal, Duckietown, IVADO, Jane Street + Depth First Learning, and Vraj Youth.

Email: bhairavmehta95[AT]gmail.com

Github: bhairavmehta95

Active Research Projects

Active Domain Randomization
Bhairav Mehta, Manfred Diaz, Florian Golemo, Christopher Pal, Liam Paull
RLDM 2019
[Github][Arxiv]

We tackle the uniform sampling assumption in domain randomization and learn a randomization strategy, looking for the most informative environments. Our method shows significant improvements in agent performance, agent generalization, sample complexity, and interpretability over the traditional domain and dynamics randomization strategies.



Duckietown

Outside of my research, I spend time working with Duckietown (Check out our simulator). I also help out with setting up infrastructure for our NIPS 2018 and ICRA 2019 AI Driving Olympics (AIDO) competitions, and even sometimes do video tutorials!



A Scalable, Flexible Augmentation of the Student Education Process
Bhairav Mehta, Adithya Ramanathan
NIPS 2018 AI for Social Good Workshop
[Paper][Slides][Talk]

We present a information retrieval approach to education and provide a end-to-end framework to go from raw text to a system where a student can learn about different topics such as History and Psychology, all while getting immediate feedback and recommendations on what to study from our system.



Past Work and Startups

Home Support Robotic Learning
Research under Satinder Singh

Aiming to train robots in simulation, I helped to develop agent environment code and DRL algorithms for a home support robot.



ICLR 2018 Reproducability Challenge
Class Project for EECS 498: Reinforcement Learning

Aiming to reproduce the results in "Parameter Space Noise for Exploration," my team and I entered into the ICLR 2018 Reproducability Challenge. Check out our final draft of the results here.




Perch and Stare: Autonomous Microaerial Vehicle Landing
Research under Larry H. Matthies at the JPL Computer Vision Group

I helped develop the main pipeline that transformed monocular camera images from the MAV's camera into a elevation height map. To then evaluate the usefulness and safety of landing sites, we used metrics like elevation, flatness and clutter to rank landing site candidates, and then used the quadcopter's motor controllers to autonomously land the vehicle.




Robotic Exploration of Space Team
Project Team advised by Edwin Olson
Video

My main project team at Michigan, I developed software features such as localization, teleoperation, and odometry using C++ and ROS for an autonomous mining rover, which we utilized during the Robotic Mining Competition.




Project Chronicle

There is no opportunity as big as education. It is an opportunity to make life-long learners; to excite students about the world; and to create explorers, scientists, entrepreneurs, entertainers, and engineers. But most students dread school. Many don't find relevance in their classes and many find their knowledge useless. Our mission at Project Chronicle is to empower those students with the ownership of stories and enhance their learning through the power of speech.

Project Chronicle has students record their telling of a prompted topic. Our platform analyzes the student's response and give immediate feedback on both the accuracy and delivery of the content. The application both challenges the students to understand the material at a much deeper level than required by typical homework and leaves them with stories they can easily remember and share. It gives them confidence. It gives them comprehension. It gives them a voice.



Teaching

EECS 445: Introduction to Machine Learning
Winter 2018

Some supplemental notes I have been writing for the class.




IFT 6757: Autonomous Vehicles (Duckietown)
Fall 2018

I help teach Duckietown at UdeM, assisting with student projects and instructional exercises.




Depth First Learning: Stein's Method in Machine Learning
Winter / Summer 2019

As an inaugural Depth First Learning Fellow I created a curriculum to help understand the use of Stein's Method in Machine Learning, an increasingly popular method in the Machine / Deep / Reinforcement Learning communities.



Mentorship

I'm always excited to work with smart people.
Here's a few folks I've been lucky to work with. Interested?

Nishant Nikhil - Summer 2019 Intern (Mila): Education and NLP
Rohan Raj - Summer 2019 Intern (Mila): Reinforcement Learning Theory
Sharath Chandra Raparthy - Summer 2019 Intern (Mila): RL + Robotics, Sim2Real Transfer




Source stolen from here