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home · events · colloquia · 2007-2008 · 

Colloquium - Grudic


Machine Learning Based Robotics
Gregory Z. Grudic
Department of Computer Science

Autonomous robot navigation in unstructured outdoor environments remains a critical challenge for tasks such as reconnaissance, search and rescue and automated driving. Completion of the DARPA Grand Challenge was an exciting step toward this goal, but competitors still required extensive use of well chosen GPS way points, sometimes only a few meters apart (similar high level information was used by teams that completed the more recent DARPA Urban Challenge). Indeed, successful navigation (between way points a few hundred meters apart) in unfamiliar dynamic outdoor environments is a key open research problem. Such Robotic tasks are characterized by a high dimensional input space that represents the world mediated by robot sensors (vision, sonar data, etc). The robot experiences millions of sensor readings at many frames per second, which must be processed and acted upon in real time. The key open questions are: 1) What information must be extracted from sensors? and, 2) How can the robot use this information to act appropriately in the world? Machine Learning techniques offer powerful tools to model complex real world situations and produce coherent behavior. Indeed many of the fundamental goals of Machine Learning are also those of Robotics, which establishes a synergy between the two fields that can serve as a catalyst for advancing theory and practice in both.

Gregory Grudic photo

This talk will describe the Machine Learning algorithms we are currently applying to autonomous navigation in unstructured outdoor environments. These represent work done under the DARPA Learning Applied to Ground Robots (LAGR) program, and NSF funded Human-to-Robot Skill transfer research. In order to travel through a new and unknown environment the robot must be able to identify safe traversable surfaces from impassable obstacles. We apply density based algorithms that use near field (within 10m of the robot) depth data from stereo vision to learn models of the image-based appearance of traversable and non-traversable terrain. The models are then applied to the far field (beyond 10m) region of the image, to facilitate long range path planning. Models are built in real-time as the robot navigates, and are intended to be used over the robot's lifetime. This Long Term Ongoing Learning, where an agent learns and maintains knowledge of the environment to improve performance over time, is a key goal of Learning and Robotics and poses many interesting questions regarding how to maintain large model sets, and when and how to apply, refine or discard models. Experimental evidence from actual robot trials shows that this approach significantly outperforms purely range based navigation.

Gregory Grudic received his PhD in Electrical and Computer Engineering at the University of British Columbia in 1997. He was a Post Doctoral Fellow at the GRASP Lab at the University of Pennsylvania between 1998 and 2001. In 2001 he joined the Department of Computer Science at the University of Colorado, where he is currently an Assistant Professor. This work is partially funded by DOD AFRL FA8650-07-C-7702, NSF IIS 0535269, and NSF CNS 043059.

Hosted by Michael Mozer.

The Department holds colloquia throughout the Fall and Spring semesters. These colloquia, open to the public, are typically held on Thursday afternoons, but sometimes occur at other times as well. If you would like to receive email notification of upcoming colloquia, subscribe to our Colloquia Mailing List. If you would like to schedule a colloquium, see Colloquium Scheduling.

Sign language interpreters are available upon request. Please contact Stephanie Morris at least five days prior to the colloquium.

See also:
Department of Computer Science
College of Engineering and Applied Science
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Boulder, CO 80309-0430 USA
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