If you want to design robots able to interact to the real world in a useful way, you will eventually bump into the problem of implementing robust object recognition, when by robust I mean able to recognize objects irrespective of (or at least able to tolerate variation in..) distance from the object, its orientation, illumination conditions, etc.
June 15, 2012|
March 14, 2012|
In the previous post, I introduced the project undertaken last semester. In this post, I will go into further detail on my particular task in the project: reinforcement learning. If you recall, the robot we wish to control is an iRobot Create (a vacuumless Roomba), which we have augmented with a web camera. The camera is able to pan to 150° in either direction from center. Read the rest of this entry »
March 12, 2012|
In the fall of 2011, I, along with Jeremy Wurbs and Annan Mozeika, initiated a project to use visual tracking and reinforcement learning to cause an iRobot Create to develop approach and avoidance behaviors. This work was done for credit in Boston University's "Topics in Adaptive Mobile Robotics" course. Read the rest of this entry »
December 8, 2011|
I was recently interviewed by Scope, a publication established in 2005 to showcase the work undertaken by the students in the MIT Graduate Program in Science Writing. The interview was about a research project led by Chi-Sang Poon, whose MIT group has designed a chip emulating in detail the dynamics of brain synapses, the junctions between neurons. Read the rest of this entry »
August 9, 2011|
This brief essay, originated by the work on the Neuromorphics Lab in the DARPA SyNAPSE project, describes our early effort in the study of alternative computing schemes that will make use of massive memristive-based devices coupled with low-power CMOS processes to efficiently compute neural activation and learning in novel computing devices. The answer was to couple fuzzy inference with dense memristive memory. This combination can provide extensive power and silicon real estate savings while maintaining a high degree of accuracy in the resulting precision of the computations. Read the rest of this entry »
February 7, 2010|
Top Gun taught us that the best and brightest pilots can perform some amazing aerobatics. Nobody seems surprised that a good pilot, with some practice, can move seamlessly from the flight maneuvers used on a Boeing 747 to those featured in Blue Angels shows. While computer autopilots have performed well in commercial aircraft for some time, however, getting an electronic computer to pull a plane successfully through an aerobatic maneuver is almost impossible, and is thus a relatively new field of research. Read the rest of this entry »
February 2, 2010|
One of the major themes in the SyNAPSE project is developing chips that can learn meaningful information, and preserve it over time. In other words: memristors can learn, but we need to ensure that they are stably learning something useful for the system they are embedded in.
Some help to solve this technological problem comes from neuroscience. The question of how can the cerebral cortex develop stable memories while at the same time incorporating new information through an organism lifetime has been a central theme in many research groups. The talk posted on Neurdon describes one of these approaches. Read the rest of this entry »
July 16, 2009|
A recent article on the WSJ (In Search for Intelligence, a Silicon Brain Twitches) reviews the Blue Brain project based at the École Polytechnique Fédérale de Lausanne in Switzerland. The Blue Brian project, led for the last four years by Henry Markram, has focused in building a biologically accurate rat cortical column. Read the rest of this entry »
June 28, 2009|
I'm a 4th-year PhD student in the Institute of Cognitive Science at The University of Louisiana at Lafayette. When I entered the program, I was mostly interested in AI and evolutionary algorithms. I wanted to evolve a Go-playing program. But my interests shifted, especially in my first year when I read Jeff Hawkins' On Intelligence. I thought it was great stuff, and I liked two things central to his framework: 1) The temporal aspect of cognition, and 2) The crucial role of feedback. He made a convincing case that every modality and skill is essentially a matter of learning and processing sequences. So that's where I started focusing my attention. Read the rest of this entry »
February 20, 2009|
The challenge of building, within a few decades, a computer chip on the scale of a patch of biological cortex is a race involving many labs in academics and industry around the world.
The basic assumption is that, in order to build machines that imitate the cortex, the intuitive way to go is capture in a chip the architecture and functional principles of cerebral cortex. Building a chip that emulates the cortex needs to solve several challenging problems. For example, how can you pack millions of processing elements and billions of synapses into a small enough chip and be able to perform computations at a speed compatible with human thought. All this must be done without consuming a lot of power. Easy, right? Read the rest of this entry »