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Institute for Robotics and Intelligent Machines (IRIM)
Institute for Robotics and Intelligent Machines (IRIM)
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ItemRobots that Need to Mislead: Biologically-inspired Machine Deception(Georgia Institute of Technology, 2012) Arkin, Ronald C.
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ItemMobbing Behavior and Deceit and its role in Bioinspired Autonomous Robotic Agents(Georgia Institute of Technology, 2012) Davis, Justin ; Arkin, Ronald C.Arabian babblers are highly preyed upon avians living in the Israeli desert. The survival of this species is contingent upon successful predator deterrence known as mobbing. Their ability to successfully defend against larger predators is the inspiration for this research with the goal of employing new models of robotic deception. Using Grafen's Dishonesty Model [3], simulation results are presented, which portend the value of this behavior in military situations.
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ItemOverriding Ethical Constraints in Lethal Autonomous Systems(Georgia Institute of Technology, 2012) Arkin, Ronald C. ; Ulam, Patrick D.This article describes the philosophy, design, and prototype implementation of an operator override system intended for use in managing unmanned robotic systems capable of lethal behavior. The ethical ramifications associated with the responsibility assignment of such a system are presented, which guide the development of the proof-of-concept system that serves as the basis for the simulation results presented herein.
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ItemAutomatic Landmark Detection for Topological Mapping Using Bayesian Surprise(Georgia Institute of Technology, 2008) Ranganathan, Ananth ; Dellaert, FrankTopological maps are graphical representations of the environment consisting of nodes that denote landmarks, and edges that represent the connectivity between the landmarks. Automatic detection of landmarks, usually special places in the environment such as gateways, in a general, sensor-independent manner has proven to be a difficult task. We present a landmark detection scheme based on the notion of “surprise” that addresses these issues. The surprise associated with a measurement is defined as the change in the current model upon updating it using the measurement. We demonstrate that surprise is large when sudden changes in the environment occur, and hence, is a good indicator of landmarks. We evaluate our landmark detector using appearance and laser measurements both qualitatively and quantitatively. Part of this evaluation is performed in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.
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ItemSquare Root SAM: Simultaneous Localization and Mapping via Square Root Information Smoothing(Georgia Institute of Technology, 2005) Dellaert, FrankSolving the SLAM problem is one way to enable a robot to explore, map, and navigate in a previously unknown environment. We investigate smoothing approaches as a viable alternative to extended Kalman filter-based solutions to the problem. In particular, we look at approaches that factorize either the associated information matrix or the measurement matrix into square root form. Such techniques have several significant advantages over the EKF: they are faster yet exact, they can be used in either batch or incremental mode, are better equipped to deal with non-linear process and measurement models, and yield the entire robot trajectory, at lower cost. In addition, in an indirect but dramatic way, column ordering heuristics automatically exploit the locality inherent in the geographic nature of the SLAM problem. In this paper, we present the theory underlying these methods, an interpretation of factorization in terms of the graphical model associated with the SLAM problem, and simulation results that underscore the potential of these methods for use in practice.
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ItemSegmental Switching Linear Dynamic Systems(Georgia Institute of Technology, 2005) Oh, Sang Min ; Rehg, James M. ; Dellaert, FrankWe introduce Segmental Switching Linear Dynamic Systems (S-SLDS), which improve on standard SLDSs by explicitly incorporating duration modeling capabilities. We show that S-SLDSs can adopt arbitrary finite-sized duration models that describe data more accurately than the geometric distributions induced by standard SLDSs. We also show that we can convert an S-SLDS to an equivalent standard SLDS with sparse structure in the resulting transition matrix. This insight makes it possible to adopt existing inference and learning algorithms for the standard SLDS models to the S-SLDS framework. As a consequence, the more powerful S-SLDS model can be adopted with only modest additional effort in most cases where an SLDS model can be applied. The experimental results on honeybee dance decoding tasks demonstrate the robust inference capabilities of the proposed S-SLDS model.
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ItemDirichlet Process based Bayesian Partition Models for Robot Topological Mapping(Georgia Institute of Technology, 2004) Ranganathan, Ananth ; Dellaert, FrankRobotic mapping involves finding a solution to the correspondence problem. A general purpose solution to this problem is as yet unavailable due to the combinatorial nature of the state space. We present a framework for computing the posterior distribution over the space of topological maps that solves the correspondence problem in the context of topological mapping. Since exact inference in this space is intractable, we present two sampling algorithms that compute sample-based representations of the posterior. Both the algorithms are built on a Bayesian product partition model that is derived from the mixture of Dirichlet processes model. Robot experiments demonstrate the applicability of the algorithms.
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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.