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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemDeep Segments: Comparisons between Scenes and their Constituent Fragments using Deep Learning(Georgia Institute of Technology, 2014-09) Doshi, Jigar ; Mason, Celeste ; Wagner, Alan ; Kira, ZsoltWe examine the problem of visual scene understanding and abstraction from first person video. This is an important problem and successful approaches would enable complex scene characterization tasks that go beyond classification, for example characterization of novel scenes in terms of previously encountered visual experiences. Our approach utilizes the final layer of a convolutional neural network as a high-level, scene specific, representation which is robust enough to noise to be used with wearable cameras. Researchers have demonstrated the use of convolutional neural networks for object recognition. Inspired by results from cognitive and neuroscience, we use output maps created by a convolutional neural network as a sparse, abstract representation of visual images. Our approach abstracts scenes into constituent segments that can be characterized by the spatial and temporal distribution of objects. We demonstrate the viability of the system on video taken from Google Glass. Experiments examining the ability of the system to determine scene similarity indicate ρ (384) = ±0:498 correlation to human evaluations and 90% accuracy on a category match problem. Finally, we demonstrate high-level scene prediction by showing that the system matches two scenes using only a few initial segments and predicts objects that will appear in subsequent segments.
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ItemIntegrated Mission Specification and Task Allocation for Robot Teams - Design and Implementation(Georgia Institute of Technology, 2006) Arkin, Ronald C. ; Endo, Yoichiro ; Ulam, Patrick D. ; Wagner, AlanAs the capabilities, range of missions, and the size of robot teams increase, the ability for a human operator to account for all the factors in these complex scenarios can become exceedingly difficult. Our previous research has studied the use of case-based reasoning (CBR) tools to assist a user in the generation of multi-robot missions. These tools, however, typically assume that the robots available for the mission are of the same type (i.e., homogeneous). We loosen this assumption through the integration of contract-net protocol (CNP) based task allocation coupled with a CBR-based mission specification wizard. Two alternative designs are explored for combining case-based mission specification and CNP-based team allocation as well as the tradeoffs that result from the selection of one of these approaches over the other.
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ItemIntegrated Mission Specification and Task Allocation for Robot Teams - Part 2: Testing and Evaluation(Georgia Institute of Technology, 2006) Arkin, Ronald C. ; Endo, Yoichiro ; Ulam, Patrick D. ; Wagner, AlanThis work presents the evaluation of two mission specification and task allocation architectures. These architectures, described in part 1 of this paper, present novel means with which to integrate a case-based reasoning (CBR) mission planner with contract net protocol (CNP) based task allocation. In the first design, the CBR and runtime-CNP architecture, the case-based mission planner generates mission plans that support necessary behaviors for CNP-based task allocation and execution. In the second design, the CBR and premission-CNP architecture, task allocation takes place during mission specification. The results of an empirical evaluation of the CBR and runtime-CNP across three naval scenarios is described. Finally, we briefly describe an earlier usability evaluation of the CBR and premission-CNP architecture using goals, operators, methods, and selection rules modeling.
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ItemMulti-Robot Communication-Sensitive Reconnaissance(Georgia Institute of Technology, 2003) Arkin, Ronald C. ; Wagner, AlanThis paper presents a method for multi-robot communication-sensitive reconnaissance. This approach utilizes collections of precompiled vector fields in parallel to coordinate a team of robots in a manner that is responsive to communication failures. Collections of vector fields are organized at the task level for reusability and generality. Different team sizes, scenarios, and task management strategies are investigated. Results indicate an acceptable reduction in communication attenuation when compared to other related methods of navigation. Online management of tasks and potential scalability are discussed.