Deep Learning on Natural Viewing Behaviors to Differentiate Children with Fetal Alcohol Spectrum Disorder

Tseng, P-H., A. Paolozza, DP.. Munoz, JN.. Reynolds, and L. Itti, "Deep Learning on Natural Viewing Behaviors to Differentiate Children with Fetal Alcohol Spectrum Disorder", Intelligent Data Engineering and Automated Learning – IDEAL 2013, vol. 8206: Springer Berlin Heidelberg, pp. 178-185, 2013.

A factor graph approach to estimation and model predictive control on Unmanned Aerial Vehicles

Ta, D-N., M. Kobilarov, and F. Dellaert, "A factor graph approach to estimation and model predictive control on Unmanned Aerial Vehicles", Unmanned Aircraft Systems (ICUAS), 2014 International Conference on, May, 2014.

Mobile Robot Navigation System in Outdoor Pedestrian Environment Using Vision-Based Road Recognition

Siagian, C. C., C. K. Chang, and L. Itti, "Mobile Robot Navigation System in Outdoor Pedestrian Environment Using Vision-Based Road Recognition", Proc. IEEE International Conference on Robotics and Automation (ICRA), May, 2013.

Mobility Erosion: High speed motion safety for mobile robots operating in off-road terrain

Karumanchi, S. B., K. Iagnemma, and S. Scheding, "Mobility Erosion: High speed motion safety for mobile robots operating in off-road terrain", Robotics and Automation (ICRA), 2013 IEEE International Conference on, May, 2013.

Video Segmentation by Tracking Many Figure-Ground Segments

Li, F., T. Kim, A. Humayun, D. Tsai, and J. J. M. Rehg, "Video Segmentation by Tracking Many Figure-Ground Segments", The IEEE International Conference on Computer Vision (ICCV), December, 2013.

Learning to Predict Gaze in Egocentric Video

Li, Y., A. Fathi, and J. J. M. Rehg, "Learning to Predict Gaze in Egocentric Video", The IEEE International Conference on Computer Vision (ICCV), December, 2013.

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Dynamics and Control Systems Lab

 

Robotic Mobility Group

 

Aerospace Robotics and Embedded Systems Laboratory

About us

We are three research groups from Georgia Tech, the Massachusetts Institute of Technology, and the University of Southern California, collaborating to perform basic research on high-speed autonomous driving.  We are most interested in researching biologically-inspired methods in the realms of both perception and control.

Acknowledgment

This work was supported by the Army Research Office under MURI Award W911NF-11-1-0046.