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Boris Sofman
PhD Student

Email address: bsofman@andrew.cmu.edu
Office: EDSH 232
Phone: (412) 268-3402
Fax: 412-268-5571

Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213

For more information, see my personal homepage.

Jump to: Research interests | Keywords | Projects | Publications

I am interested in the study of truly autonomous robotic systems operating in complex, unstructured environments. These systems need to interpret the environment, make efficient plans, and react to uncertainties and their own mistakes. The complexity of many environments, however, makes predetermined behaviors insufficient in many situations. I am researching how on-line learning techniques can enable robots to adjust to such situations by improving their performance with experience. This opens up all sorts of interesting opportunities in pushing the versatility of such systems.

3-D perception, artificial intelligence, field robotics, machine learning, mobile robots, motion planning, multi-agent systems, obstacle avoidance, and planning

• UGCV PerceptOR Integrated - The UPI (UGCV PerceptOR Integrated) program integrates and enhances the results from UGCV and PerceptOR to increase the speed and autonomy of unmanned ground vehicles operating in complex terrain. By combining the inherent mobility of Spinner with advanced perception techniques including the use of learning and prior terrain data, the UPI program stresses system design across vehicle, sensors and software so that the strengths of one component compensate for the weaknesses of another.

Note: This list may not be comprehensive. It contains only those publications in the RI publications database. Entries are listed in reverse chronological order.

• Improving Robot Navigation Through Self-Supervised Online Learning
  B. Sofman, E.L. Ratliff, J. Bagnell, J. Cole, N. Vandapel, and A. Stentz
  Journal of Field Robotics, Vol. 23, No. 12, December, 2006. [Abstract]
  Download: pdf [2114 KB] copyrighted

• Experimental Analysis of Overhead Data Processing To Support Long Range Navigation
  D. Silver, B. Sofman, N. Vandapel, J. Bagnell, and A. Stentz
  IEEE International Conference on Intelligent Robots and Systems (IROS), October, 2006, pp. 2443 - 2450. [Abstract]
  Download: pdf [1196 KB] copyrighted

• Improving Robot Navigation Through Self-Supervised Online Learning
  B. Sofman, E.L. Ratliff, J. Bagnell, N. Vandapel, and A. Stentz
  Proceedings of Robotics: Science and Systems, August, 2006. [Abstract]
  Download: pdf [1683 KB] copyrighted

• Terrain Classification from Aerial Data to Support Ground Vehicle Navigation
  B. Sofman, J. Bagnell, A. Stentz, and N. Vandapel
  tech. report CMU-RI-TR-05-39, Robotics Institute, Carnegie Mellon University, January, 2006. [Abstract]
  Download: pdf [697 KB] copyrighted