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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemData-Driven MCMC for Learning and Inference in Switching Linear Dynamic Systems(Georgia Institute of Technology, 2005-07) Oh, Sang Min ; Rehg, James M. ; Balch, Tucker ; Dellaert, FrankSwitching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS has significantly more descriptive power than an HMM, but inference in SLDS models is computationally intractable. This paper describes a novel inference algorithm for SLDS models based on the Data- Driven MCMC paradigm. We describe a new proposal distribution which substantially increases the convergence speed. Comparisons to standard deterministic approximation methods demonstrate the improved accuracy of our new approach. We apply our approach to the problem of learning an SLDS model of the bee dance. Honeybees communicate the location and distance to food sources through a dance that takes place within the hive. We learn SLDS model parameters from tracking data which is automatically extracted from video. We then demonstrate the ability to successfully segment novel bee dances into their constituent parts, effectively decoding the dance of the bees.
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ItemWhat Are the Ants Doing? Vision-Based Tracking and Reconstruction of Control Programs(Georgia Institute of Technology, 2005-04) Balch, Tucker ; Dellaert, Frank ; Delmotte, Florent ; Khan, Zia ; Egerstedt, Magnus B.In this paper, we study the problem of going from a real-world, multi-agent system to the generation of control programs in an automatic fashion. In particular, a computer vision system is presented, capable of simultaneously tracking multiple agents, such as social insects. Moreover, the data obtained from this system is fed into a mode-reconstruction module that generates low-complexity control programs, i.e. strings of symbolic descriptions of control-interrupt pairs, consistent with the empirical data. The result is a mechanism for going from the real system to an executable implementation that can be used for controlling multiple mobile robots.
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ItemAn MCMC-based Particle Filter for Tracking Multiple Interacting Targets(Georgia Institute of Technology, 2003) Khan, Zia ; Balch, Tucker ; Dellaert, FrankWe describe a Markov chain Monte Carlo based particle filter that effectively deals with interacting targets, i.e., targets that are influenced by the proximity and/or behavior of other targets. Such interactions cause problems for traditional approaches to the data association problem. In response, we developed a joint tracker that includes a more sophisticated motion model to maintain the identity of targets throughout an interaction, drastically reducing tracker failures. The paper presents two main contributions: (1) we show how a Markov random field (MRF) motion prior, built on the fly at each time step, can substantially improve tracking when targets interact, and (2) we show how this can be done efficiently using Markov chain Monte Carlo (MCMC) sampling. We prove that incorporating an MRF to model interactions is equivalent to adding an additional interaction factor to the importance weights in a joint particle filter. Since a joint particle filter suffers from exponential complexity in the number of tracked targets, we replace the traditional importance sampling step in the particle filter with an MCMC sampling step. The resulting filter deals efficiently and effectively with complicated interactions when targets approach each other. We present both qualitative and quantitative results to substantiate the claims made in the paper, including a large scale experiment on a video-sequence of over 10,000 frames in length.
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ItemEfficient Particle Filter-Based Tracking of Multiple Interacting Targets Using an MRF-based Motion Model(Georgia Institute of Technology, 2003) Balch, Tucker ; Dellaert, Frank ; Khan, ZiaWe describe a multiple hypothesis particle filter for tracking targets that will be influenced by the proximity and/or behavior of other targets. Our contribution is to show how a Markov random field motion prior, built on the fly at each time step, can model these interactions to enable more accurate tracking. We present results for a social insect tracking application, where we model the domain knowledge that two targets cannot occupy the same space, and targets will actively avoid collisions. We show that using this model improves track quality and efficiency. Unfortunately, the joint particle tracker we propose suffers from exponential complexity in the number of tracked targets. An approximation to the joint filter, however, consisting of multiple nearly independent particle filters can provide similar track quality at substantially lower computational cost.
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ItemThe Georgia Tech Yellow Jackets: A Marsupial Team for Urban Search and Rescue(Georgia Institute of Technology, 2002) Alegre, Fernando ; Balch, Tucker ; Berhault, Marc ; Dellaert, Frank ; Kaess, Michael ; McGuire, Robert ; Merrill, Ernest ; Moshkina, Lilia ; Ravichandran, Ram ; Walker, DanielWe describe our entry in the AAAI 2002 Urban Search and Rescue (USAR) competition, a marsupial team consisting of a larger wheeled robot and several small legged robots, carried around by the larger robot. This setup exploits complimentary strengths of each robot type in a challenging domain. We describe both the hardware and software architecture, and the on-board real-time mapping which forms the basis of accurate victim-localization crucial to the USAR domain. We also evaluate what challenges remain to be resolved in order to deploy search and rescue robots in realistic scenarios.