I am a undergraduate senior at the University of Michigan working under Satinder Singh and Matthew Johnson-Roberson.
Between January 2017 and June 2017, I left the University of
Michigan to spend time at the Jet Propulsion Laboratory, working
full-time under Larry H. Matthies in the Computer Vision Group. I will be spending Summer 2018 in Montreal at MILA under the supervision of Liam Paull.
My research interests are focused on the intersection of robotics
and reinforcement learning, including the simulation-transfer
problem, lifelong learning, and model-based reinforcement 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.
Most recently, I started writing on Medium.
Like many others, I believe there is no middle-ground in learning machine learning; either you watch a high level overview, or
you read a paper. Here is some of my work trying to help fix
that issue: Model Based Reinforcement Learning. Similarly, here are some supplemental notes I have been writing as a TA for Michigan's Machine Learning class.
Multiagent Robotic RL in Gazebo
Bhairav Mehta, Matthew Johnson-Roberson
Undergraduate Senior Thesis: In Progress
Orignal Project Proposal
Reinforcement learning, and other aspects of machine learning, have allowed researchers
to build robots that can function in under-modeled, dynamic,
or cluttered environments. Yet, machine learning is built upon
the premise that both positive and negative examples are presented
to the system. In some cases, this constraint of training data
is financially expensive or is dangerous to obtain. While work
has been done in simulators, the bulk of work has stayed within
simulators not as popular or familiar to the robotics community.
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.
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. I am exploring model-based algorithms for self-supervised learning.
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.
EECS 445: Introduction to Machine Learning
Machine Learning is a great course, but there's often too much to cover, and we can't always explain everything fully when teaching the course at Michigan. As a result, I've been writing CS229-esque notes on tangential or advanced topics, for interested readers in our Introduction to Machine Learning class. Check out some work here.