I moved back to Pittsburgh this summer (2014) to start a company called CapSen Robotics. Please email me at
"jared dot glover at capsenrobotics dot com" if you'd like more information.
I just completed my PhD in the Learning and Intelligent Systems lab at MIT.
Here are some projects that I've worked on over the years. Feel free to email me at "jglov at mit dot edu" if
you have any questions or would like help using any of my code, or if you just want to say hi!
The Bingham distribution is a type of probability distribution on directional data.
I frequently use it in my research to represent uncertainty on 3-D orientations (parameterized
by unit quaternions), so I developed an open-source library in C and Matlab, called the
Bingham Statistics Library.
Please see my research page or one of the papers below for more information.
With the recent availability of cheap RGB-D cameras like the Kinect, robust 3-D object recognition
(of rigid object instances) is becoming closer to a reality every day. A major focus of my research
since 2010 has been on geometric object detection in cluttered RGB-D images, where the Bingham distribution
has proven to be a useful tool for processing the orientation information (e.g., surface normals and
principal curvatures) in each scene.
My current project is to get a robot to play ping pong. More broadly, I'm
interested in dynamic manipulation for robots. One finds dynamic manipulation tasks
everywhere--chopping vegetables, stirring ingredients in a bowl, flipping hamburgers, moving furniture,
playing sports, and so on. Furthermore, many non-dynamic manipulation tasks become
dynamic when the robot is pushed to its limits: for example, when we want the robot to
move very quickly, or to manipulate very heavy objects.
The Bingham distribution turns out to be very useful for robotic ping pong, because it can be used
to create a very robust algorithm for tracking the spin on the ping pong ball.
Another key task in cluttered environments is to estimate what the hidden parts of objects look like.
This ability is useful for manipulation, where the robot must reach behind an object to manipulate it.
It is also useful for object recognition and localization, particularly for ruling out object
configurations which are "geometrically impossible"--i.e. which would cause two or more objects to occupy
the same space, or which would cause parts of an object to occupy space which we know to be empty.
During my undergrad at Carnegie Mellon, I developed a robotic walker named IMP
(Intelligent Mobility Platform) as part of the Nursebot project.
The walker was able to self-navigate to park itself and return to the user at the touch of a remote. Then, when
the user wanted to get up and go, it could provide either audio or visual navigational guidance to the user.