Boris Sofman

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In mobile robotics, there are often features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations. Overhead imagery data, for instance, have the potential to greatly enhance autonomous robot navigation in complex outdoor environments. In practice, reliable and effective(More)
A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”[13, 28]. The library is either evaluated on a fixed number of candidate choices at runtime (e.g. path set selection for planning) or by iterating through a sequence of feasible choices until success is achieved (e.g. grasp(More)
Novelty detection is often treated as a one-class classification problem: how to segment a data set of examples from everything else that would be considered novel or abnormal. Almost all existing novelty detection techniques, however, suffer from diminished performance when the number of less relevant, redundant or noisy features increases, as often the(More)
Long range navigation by unmanned ground vehicles continues to challenge the robotics community. Efficient navigation requires not only intelligent on-board perception and planning systems, but also the effective use of prior knowledge of the vehicle's environment. This paper describes a system for supporting unmanned ground vehicle navigation through the(More)
Autonomous navigation by a mobile robot through L natural, unstructured terrain is one of the premier k challenges in field robotics. Tremendous advances V in autonomous navigation have been made recently in field robotics. Machine learning has played an increasingly important role in these advances. The Defense Advanced Research Projects Agency (DARPA)(More)
Sensory perception for unmanned ground vehicle navigation has received great attention from the robotics community. However, sensors mounted on the vehicle are regularly viewpoint impaired. A vehicle navigating at high speeds in offroad environments may be unable to react to negative obstacles such as large holes and cliffs. One approach to address this(More)
The high cost of damaging an expensive robot or injuring people or equipment in its environment make even rare failures unacceptable in many mobile robot applications. Often the objects that pose the highest risk for a mobile robot are those that were not present throughout previous successful traversals of an environment. Change detection, a closely(More)
Autonomous navigation by a mobile robot through natural, unstructured terrain is one of the premier challenges in field robotics. The DARPA UPI program was tasked with advancing the state of the art in robust autonomous performance through challenging and widely varying environments. In order to accomplish this goal, machine learning techniques were heavily(More)