I am a research intern at MILA under the supervision of Liam Paull and Christopher Pal, working on Duckietown and sim2real transfer. 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.
My research interests are concentrated at the intersection of robotics and reinforcement learning, including the simulation-transfer problem and model-based reinforcement learning, but I am also excited by research areas such as lifelong learning and deep learning.
In my free time, I develop the machine learning algorithms behind an edtech startup
I cofounded named Project Chronicle. We have been funded by
the University of Michigan CSE Department and the 1517 Fund.
I do my research at MILA mostly working with Duckietown (Check out our simulator). I also help out with setting up infrastructure for our upcoming NIPS 2018 and ICRA 2019 AI Driving Olympics (AIDO) competitions.
Multiagent Robotic RL in Gazebo
Bhairav Mehta, Matthew Johnson-Roberson
Undergraduate Senior Thesis
[Proposal] [Github: Coming Soon!]
This project, building upon the work of
Erle Robotics, aims to give researchers the ability to train teams of robots
in simulation using Gazebo, a more well known physics simulator
(w.r.t robotics research). The project, with a modular interface
to swap out RL algorithms, robots, and environments, aims to
boost interest by providing a solution that does not require
porting URDF models and rebuilding robot environments.
A Scalable, Flexible Augmentation of the Student Education Process
Bhairav Mehta, Adithya Ramanathan
Extended Abstract [Full Paper Soon]
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.
Home Support Robotic Learning
Research under Satinder Singh
Aiming to train robots in simulation, I have been developing 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
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.
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.