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Boris Sofman

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4 papers

2 author rows

AAAI Conference 2012 Conference Paper

Efficient Optimization of Control Libraries

Debadeepta Dey
Tian Liu
Boris Sofman
James Bagnell

A popular approach to high dimensional control problems in robotics uses a library of candidate “maneuvers” or “trajectories”. 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 selection). The performance of the library relies heavily on the content and order of the sequence of candidates. We propose a provably efficient method to optimize such libraries, leveraging recent advances in optimizing sub-modular functions of sequences. This approach is demonstrated on two important problems: mobile robot navigation and manipulator grasp set selection. In the first case, performance can be improved by choosing a subset of candidates which optimizes the metric under consideration (cost of traversal). In the second case, performance can be optimized by minimizing the depth in the list that is searched before a successful candidate is found. Our method can be used in both on-line and batch settings with provable performance guarantees, and can be run in an anytime manner to handle real-time constraints.

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ICRA Conference 2011 Conference Paper

Segmentation-based online change detection for mobile robots

Bradford Neuman
Boris Sofman
Anthony Stentz
J. Andrew Bagnell

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 related problem to novelty detection, is therefore of high importance to many mobile robotic applications that require a robot to operate repeatedly in the same environment. We present a novel algorithm for performing online change detection based on a previously developed robust online novelty detection system that uses a learned lower-dimensional representation of the feature space to perform measures of similarity. We then further improve this change detection system by incorporating online scene segmentation to better utilize contextual information in the environment. We validate these approaches through extensive experiments onboard a large outdoor mobile robot. Our results show that our approaches are robust to noisy sensor data and moderate registration errors and maintain their performance across diverse natural environments and conditions.

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ICRA Conference 2010 Conference Paper

Anytime online novelty detection for vehicle safeguarding

Boris Sofman
J. Andrew Bagnell
Anthony Stentz

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 case with high-dimensional feature spaces. Many of these algorithms are also not suited for online use, a trait that is highly desirable for many robotic applications. We present a novelty detection algorithm that is able to address this sensitivity to high feature dimensionality by utilizing prior class information within the training set. Additionally, our anytime algorithm is well suited for online use when a constantly adjusting environmental model is beneficial. We apply this algorithm to online detection of novel perception system input on an outdoor mobile robot and argue such abilities could be key in increasing the real-world applications and impact of mobile robotics 1.

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IROS Conference 2006 Conference Paper

Experimental Analysis of Overhead Data Processing To Support Long Range Navigation

David Silver 0002
Boris Sofman
Nicolas Vandapel
J. Andrew Bagnell
Anthony Stentz

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 use of heterogeneous overhead data. Semantic information is obtained through supervised classification, and vehicle mobility is predicted from available geometric data. This approach is demonstrated and validated through over 50 kilometers of autonomous traversal through complex natural environments

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