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I firmly believe that social interactions is of vital importance in academic research as well as other avenues of life.  In support of this belief, I have tried to, where I can, to create the infrastructure necessary to facilitate discussion, collaboration, and communication between students from different research groups.  Below is a list of some of my preliminary activities in this direction:
!!!Robotics Reading Group
As a new graduate student, after hearing the story about how the old robotics reading group started as a means to study for quals from [[Keith O'Hara|]], I decided to revive one for the same purpose.  We mostly read from the old intelligent system quals list, but we have recently transitioned to papers relevant to our research interests as the new robotics Ph.D.'s quals list have not been finalized yet.  Currently, our group meets on Thursdays @ 6 pm in TSRB room 222.  [[link|]]
!!!Robotics Students Mailing List
I created and am managing a mailing list for robotics students in the new Georgia Tech robotics program.  [[link|]]
<html><center><a href=files/><img width=80% src=files/full_res/ant_partiview.jpg></a></center></html>
Trying to get a better grasp of why one of my trackers was failing I created a visualization using [[partiview|]] to see how useful reasoning in time would be for tracking.  Seeing the visual did help somewhat in understanding the problem, but it was not as informative as I thought it would be.  In the linked video, each trail of dots is one ant and the camera is moving forward in time looking at the past where each dot in the data represents an ant pixel* at a particular moment in time (u,v,t).

  *determined using simple color thresholding, and image differencing with the former represented by gray, and the later by orange dots.

''Abstract'': We present a method for learning a human understandable, //executable model// of an agent's behavior using observations of its interaction with the environment. By executable we mean that the model is suitable for direct execution by an agent. Traditional models of behavior used for recognition tasks (e.g., Hidden Markov Models) are insufficent because they cannot respond to input from the environment. We train an Input/Output Hidden Markov Model where the output distributions are mixtures of learned low level actions and the transition distributions are conditional on features calculated from the agent's sensors. We show that we are able to recover both the behavior and human- understandable structure of a simulated model inspired by animal behavior studies. We also present a novel training method that combines multiple EM trials through discrete optimization. 

CRIM stands for Center for Robotics and Intelligent Machines.  Officially, this is RIM but taking into account modern definitions of the word I find the acronym to be unfortunate and in bad taste.

''Abstract'': A new platform for mobile manipulation consisting of a Segway RMP base and a Kuka KR-5 sixx manipulator was developed at the Georgia Institute of Technology in the context of a class. Students formed three teams whose goal was to design a system capable of autonomously serving coffee. Each team took a different approach to the problem in terms of system architecture, visual recognition, and grasping procedure. The proposed poster will present approaches used by the students and their merits with respect to this task.  
haidai @ cc . gatech . edu 
haidai @ gmail . com
<html><center><img width=70% src="files/designs/hai_robots.gif></center></html>
| [img[files/edu_rob.jpg]] | [img[files/egg.jpg]] |
Unable to resist a good brain storming challenge and the chance to influence the decision of a robot that will be mass produced, when the BORG lab needed prospective designs for a robot engaging to computer science undergraduates, I sat down with another friend to sketch out some possible concept robots.
I worked in the summer of 2006 with Dr. Candace Sidner and Dr. Christopher Lee on the interactive penguin robot Mel at Mitsubishi Electric Research Laboratories (MERL).  We were able to successfully programmed Mel to navigate between offices to locate Dr. Sidner (Candy) using face recognition and bring her back to demonstrate another piece of technology developed at MERL.

Mel was subsequently upgraded to become Melvin using a humanoid body developed by [[Robomotio|]].
|[img[files/edu_rob.jpg]]| [img[files/heavy_metal.jpg]] |
| [[Educational Robot]] | [[Rescue Robot]] |
| [img[files/reddy_s.jpg]] | [img[files/wiring_board.jpg]] |
| MERL's [[Mel]] | [[Wiring]] |
| [img[files/ant_partiview.jpg]] | |
| Visualization of [[Ant Trails]] ([[video|files/]]) | |

* my [[|]] account
* my good friend Felix's blog about cooking: [[cleaver feaver|]]
* my labmate Travis's blog about technology: [[hizook|]]
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| [img[files/behavior_learn.jpg]] | [img[files/bender_mobip.jpg]] |
| [[Behavior Learning]] | [[Coffee Retrieval]] |
|  |  |
| [img[files/visual_odometry.jpg]] | [img[files/point_click_robot.jpg]] |
| [[Visual Odometry]] | Point-click Interface |

Hai Nguyen, Advait Jain, Cressel Anderson, and Charles C. Kemp. ''A Clickable World: Behavior Selection Through Pointing and Context for Mobile Manipulation'' , IEEE/RJS International Intelligent Robots and Systems (IROS), 2008.

Hai Nguyen, Cressel Anderson, Alexander Trevor, Advait Jain, Zhe Xu, Charles C. Kemp. ''~El-E: An Assistive Robot that Fetches Objects from Flat Surfaces''.  ~Human-Robot Interaction Workshop, 2008.

Charles C. Kemp, Cressel Anderson, Hai Nguyen, and Alex Trevor. ''A point-and-click interface for the real world: Laser designation of objects for mobile manipulation''.  ~Human-Robot Interaction, 2008. 

Richard Roberts, Hai Nguyen, Niyant Krishnamurthi, and Tucker Balch. ''Memory-based learning for visual odometry''. International Conference on Robotics and Automation, 2008. 

''A new mobile manipulation platform for automatic coffee retrieval''. In Robotics Science and Systems Manipulation Workshop, 2007. 

Andrew Guillory, Hai Nguyen, Tucker Balch, and Jr. Charles Lee Isbell. ''Learning executable agent behaviors from observation''. In Autonomous Agents and Multiagent Systems, 2006. 
| [img[files/heavy_metal_render.jpg]] | [img[files/vex_climb.png]] |
| Heavy Metal concept (Fox5 News [[video|files/RoboCupFox5.wmv]], testing [[videos|]]) | unnamed robot ([[video|files/]]) |
I helped to spec, brainstorm, build, and program Geogia Tech's robot rescue entry to Robocup 2007.  Being a design of compromises, it did not do well.  

During that time, to demonstrate that heavy metal was not the way to go, I also dabbled in constructing high mobility platforms using parts from the Vex Robotics kit.  Inspired by the segmented designs of most insects and this centipede robot ([[video|files/]]), the robot shown to the right was powered with 6 hobby servos modified for continuous rotation, and has a passive hinge in the middle that allows it to conform to the terrain.  Not having worked with servos before, I was surprised to find that 6 was enough power for this robot to be able to drag me sitting in an office chair through the BORG lab.  Although just a preliminary model, it was able to climb the Vex robotics box.
Robotics Ph.D. Student @ Georgia Tech
[[Hai Nguyen|Welcome]]
I have written a fair amount of software.  You might, as others and I have, find the following to be useful.
!!!Shard Tracker

<html><center><img width=70% src="files/full_res/btracking.jpg"></center></html>
From my work with Prof. [[Tucker Balch|]], this tracker was created to provide the position and orientations of multiple nearly identical appearing agents moving in a 2D plane.  In our work, this tracker is being used to track the movements of ants and access their interaction rate.  

Due to the tracker being written in the language OCAML using Linux libraries, I packaged it in a ~LiveCD so that anyone, notably those in the biology community, can run it using any standard PC supported by Ubuntu Linux.  Eventually, this project will be hosted on the BORG lab's main [[Biotracking|]] website, but if you would like a copy to use for research purposes, send me an email with your address. [[documentation|shard_manual/shard_manual.html]]
!!!Java Video Read/Write Support
Accessing any sorts of media in Java is often an exercise in frustration due to the bloated JMF API.  In addition, even though JMF attempts to be cross platform, there is not support for reading any of the popular codecs on most platforms.  In my trying to use JMF for computer vision, I have created this set of classes to interface Java with [[OpenCV|]] allowing Java to access to all video formats and hardware available to [[OpenCV|]].  To use this, first install [[OpenCV|]] then put these classes into your ~JAVA_PATH. [[download|files/]]
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<html><center><img width=90% src=files/full_res/visual_odometry.jpg></center></html>

''Abstract'': We present a technique for estimating the ego- motion of a mobile robot using memory-based learning and only a monocular camera. Unlike other approaches that rely heavily on camera calibration and geometry to calculate trajectory, our method learns a mapping from optical flow to platform velocity and turn rate. We developed an efficient algorithm to compute high-quality sparse optical flow, and techniques for using this optical flow as input to memory-based learning. In addition, we employ a voting scheme of many learners that use random subsets of the optical flow state dimensionality, in order to greatly reduce this dimensionality. Finally, we perform experiments in which we examine the mapping learned for visual odometry, investigate the effects of varying the reduced dimensionality of the optical flow state, and quantify the accuracy of two variations of our learner scheme. Our results indicate that our learning scheme estimates visual odometry by learning a mapping from the ground plane, and that a certain minimum dimensionality of the optical flow state is required for the most accurate results. In addition, we show that while a memory-based learning method cannot estimate visual odometry as accurately as recent geometric methods, it is possible to learn, with no prior information and no explicit model, camera calibration and position, and complicated mappings that take advantage of properties of the environment. 
<html><center><img width=100% src=files/nytimes.jpg></center></html>
I am a Ph.D. student in the ''Center for Robotics and Intelligent Machines'' ([[CRIM]]).  My research interests include ''mobile manipulation'', ''machine learning'', ''computer vision'', and ''multi-agent systems''.  Currently, I work with Prof. Charles Kemp in the [[Healthcare Robotics Lab|]], a unit under the department of [[Biomedical Engineering|]] at Georgia Tech.

I am generally interested in everything that can aid in creating intelligent, useful, and robust robots that are able to to operate in the real world.  To aid in this goal, I minored in ''cognitive psychology'' as an undergraduate, and is currently minoring in ''neuroscience'' as a graduate student.  Additionally, I work with Prof. Tucker Balch studying ants using computer vision and machine learning.  Due to this biology heavy background, many of my solutions in robotics tend to have a biological flavor, which has so far been an enormous asset in my work.

I have recently bought a couple of [[wiring boards|]] to experiment in creating interactive devices.  Although there is currently not enough time to pursue this avenue, hopefully, there will be time in the spring :)