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Автор	Тема: На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:37 PART 8: R O B O T I C S and V I S I O N 1810p [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:38 PART-8: ROBOTICS and VISION [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:38 PART-8: ROBOTICS and VISION: =301=> 1811p Adversarial Uncertainty in Multi-Robot Patrol, N.Agmon, S.Kraus, GA Kaminka, Vladimir Sadov, http://ijcai.org/papers09/Abstracts/301.html We study the problem of multi-robot perimeter patrol in adversarial environments, under uncertainty of adversarial behavior. The robots patrol around a closed area using a nondeterministic patrol algorithm. The adversary's choice of penetration point depends on the knowledge it obtained on the patrolling algorithm and its weakness points. Previous work investigated full knowledge and zero knowledge adversaries, and the impact of their knowledge on the optimal algorithm for the robots. However, realistically the knowledge obtained by the adversary is neither zero nor full, and therefore it will have uncertainty in its choice of penetration points. This paper considers these cases, and offers several approaches to bounding the level of uncertainty of the adversary, and its influence on the optimal patrol algorithm. We provide theoretical results that justify these approaches, and empirical results that show the performance of the derived algorithms used by simulated robots working against humans playing the role of the adversary is several different settings. text: http://ijcai.org/papers09/Papers/IJCAI09-301.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:38 PART-8: ROBOTICS and VISION: =302=> 1818p Evaluating Description and Reference Strategies in a Cooperative Human-Robot Dialogue System, Mary Ellen Foster, Manuel Giuliani, Amy Isard, Colin Matheson, Jon Oberlander, Alois Knoll, http://ijcai.org/papers09/Abstracts/302.html We present a human-robot dialogue system that enables a robot to work together with a human user to build wooden construction toys. We then describe a study which assessed the responses of naive users to output that varied along two dimensions: the method of describing an assembly plan (pre-order or post-order), and the method of referring to objects in the world (basic and full). Varying both of these factors produced significant results: subjects using the system that employed a pre-order description strategy asked for instructions to be repeated significantly less often than those who experienced the post-order strategy, while the subjects who heard references generated by the full reference strategy judged the robot's instructions to be significantly more understandable than did those who heard the output of the basic strategy. text: http://ijcai.org/papers09/Papers/IJCAI09-302.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:38 PART-8: ROBOTICS and VISION: =303=> 1824p Incremental Phi*: Incremental Any-Angle Path Planning on Grids, Alex Nash, Sven Koenig, Maxim Likhachev, http://ijcai.org/papers09/Abstracts/303.html We study path planning on grids with blocked and unblocked cells. Any-angle path-planning algorithms find short paths fast because they propagate information along grid edges without constraining the resulting paths to grid edges. Incremental path-planning algorithms solve a series of similar path-planning problems faster than repeated single-shot searches because they reuse information from the previous search to speed up the next one. In this paper, we combine these ideas by making the any-angle path-planning algorithm Basic Theta* incremental. This is non-trivial because Basic Theta* does not fit the standard assumption that the parent of a vertex in the search tree must also be its neighbor. We present Incremental Phi* and show experimentally that it can speed up Basic Theta* by about one order of magnitude for path planning with the freespace assumption. text: http://ijcai.org/papers09/Papers/IJCAI09-303.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:38 PART-8: ROBOTICS and VISION: =304=> 1831p Information-Lookahead Planning for AUV Mapping, Zeyn A. Saigol, Richard W. Dearden, Jeremy L. Wyatt, Bramley J. Murton, http://ijcai.org/papers09/Abstracts/304.html Exploration for robotic mapping is typically handled using greedy entropy reduction. Here we show how to apply information lookahead planning to a challenging instance of this problem in which an Autonomous Underwater Vehicle (AUV) maps hydrothermal vents. Given a simulation of vent behaviour we derive an observation function to turn the planning for mapping problem into a POMDP. We test a variety of information state MDP algorithms against greedy, systematic and reactive search strategies. We show that directly rewarding the AUV for visiting vents induces effective mapping strategies. We evaluate the algorithms in simulation and show that our information lookahead method outperforms the others. text: http://ijcai.org/papers09/Papers/IJCAI09-304.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:39 PART-8: ROBOTICS and VISION: =305=> 1837p Self-Supervised Aerial Images Analysis for Extracting Parking lot Structure, Young-Woo Seo, Nathan Ratliff, Chris Urmson, http://ijcai.org/papers09/Abstracts/305.html Road network information simplifies autonomous driving by providing strong priors about environments. It informs a robotic vehicle with where it can drive, models of what can be expected, and contextual cues that influence driving behaviors. Currently, however, road network information is manually generated using a combination of GPS survey and aerial imagery. These manual techniques are labor intensive and error prone. To full exploit the benefits of digital imagery, these processes should be automated. As a step toward this goal, we present an algorithm that extracts the structure of parking lot visible from a given aerial image. To minimize human intervention in the use of aerial imagery, we devise a self-supervised learning algorithm that automatically generates a set of parking spot templates to learn the appearance of a parking lot and estimates the structure of the parking lot from the learned model. The data set extracted from a single image alone is too small to sufficiently learn an accurate parking spot model. However, strong priors trained using large data sets collected across multiple images dramatically improvce performance. Our self-supervised approach outperforms the prior alone by adapting the distribution of examples toward that found in the current image. A thorough empirical analysis compares leading state-of-the-art learning techniques on this problem. text: http://ijcai.org/papers09/Papers/IJCAI09-305.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:39 PART-8: ROBOTICS and VISION: =306=> 1843p Nonmyopic Adaptive Informative Path Planning for Multiple Robots, Amarjeet Singh, Andreas Krause, William J. Kaiser, http://ijcai.org/papers09/Abstracts/306.html Many robotic path planning applications, such as search and rescue, involve uncertain environments with complex dynamics that can be only partially observed. When selecting the best subset of observation locations subject to constrained resources (such as limited time or battery capacity) it is an important problem to trade off exploration (gathering information about the environment) and exploitation (using the current knowledge about the environment most effectively) for efficiently observing these environments. Even the nonadaptive setting, where paths are planned before observations are made, is NP-hard, and has been subject to much research. In this paper, we present a novel approach to adaptive informative path planning that addresses this exploration-exploitation tradeoff. Our approach is nonmyopic, i.e. it plans ahead for possible observations that can be made in the future. We quantify the benefit of exploration through the “adaptivity gap” between an adaptive and a nonadaptive algorithm in terms of the uncertainty in the environment. Exploiting the submodularity (a diminishing returns property) and locality properties of the objective function, we develop an algorithm that performs provably near-optimally in settings where the adaptivity gap is small. In case of large gap, we use an objective function that simultaneously optimizes paths for exploration and exploitation. We also provide an algorithm to extend any single robot algorithm for adaptive informative path planning to the multi robot setting while approximately preserving the theoretical guarantee of the single robot algorithm. We extensively evaluate our approach on a search and rescue domain and a scientific monitoring problem using a real robotic system. text: http://ijcai.org/papers09/Papers/IJCAI09-306.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:39 PART-8: ROBOTICS and VISION: =307=> 1851p Learning Kinematic Models for Articulated Objects, Jürgen Sturm, Vijay Pradeep, Cyrill Stachniss, Christian Plagemann, Kurt Konolige, Wolfram Burgard, http://ijcai.org/papers09/Abstracts/307.html Robots operating in home environments must be able to interact with articulated objects such as doors or drawers. Ideally, robots are able to autonomously infer articulation models by observation. In this paper, we present an approach to learn kinematic models by inferring the connectivity of rigid parts and the articulation models for the corresponding links. Our method uses a mixture of parameterized and parameter-free (Gaussian process) representations and finds low-dimensional manifolds that provide the best explanation of the given observations. Our approach has been implemented and evaluated using real data obtained in various realistic home environment settings. text: http://ijcai.org/papers09/Papers/IJCAI09-307.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:39 PART-8: ROBOTICS and VISION: =308=> 1857p A Computational Model for the Alignment of Hierarchical Scene Representations in Human-Robot Interaction, Agnes Swadzba, Constanze Vorwerg, Sven Wachsmuth, Gert Rickheit, http://ijcai.org/papers09/Abstracts/308.html The ultimate goal of human-robot interaction is to enable the robot to seamlessly communicate with a human in a natural human-like fashion. Most work in this field concentrates on the speech interpretation and gesture recognition side assuming that a propositional scene representation is available. Less work was dedicated to the extraction of relevant scene structures that underlies these propositions. As a consequence, most approaches are restricted to place recognition or simple table top settings and do not generalize to more complex room setups. In this paper, we propose a hierarchical spatial model that is empirically motivated from psycholinguistic studies. Using this model the robot is able to extract scene structures from a time-of-flight depth sensor and adjust its spatial scene representation by taking verbal statements about partial scene aspects into account. Without assuming any pre-known model of the specific room, we show that the system aligns its sensor-based room representation to a semantically meaningful representation typically used by the human descriptor. text: http://ijcai.org/papers09/Papers/IJCAI09-308.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:39 PART-8: ROBOTICS and VISION: =309=> 1864p Domain-Guided Novelty Detection for Autonomous Exploration, David Ray Thompson, http://ijcai.org/papers09/Abstracts/309.html Here novelty detection identifies salient image features to guide autonomous robotic exploration. There is little advance knowledge of the features in the scene or the proportion that should count as outliers. A new algorithm addresses this ambiguity by modeling novel data in advance and characterizing regular data at run time. Detection thresholds adapt dynamically to reduce misclassification risk while accommodating homogeneous and heterogeneous scenes. Experiments demonstrate the technique on a representative set of navigation images from the Mars Exploration Rover "Opportunity." An efficient image analysis procedure filters each image using the integral transform. Pixel-level features are aggregated into covariance descriptors that represent larger regions. Finally, a distance metric derived from generalized eigenvalues permits novelty detection with kernel density estimation. Results suggest that exploiting training examples of novel data can improve performance in this domain. text: http://ijcai.org/papers09/Papers/IJCAI09-309.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:39 PART-8: ROBOTICS and VISION: =310=> 1870p Tractable Multi-Agent Path Planning on Grid Maps, Ko-Hsin Cindy Wang, Adi Botea, http://ijcai.org/papers09/Abstracts/310.html Multi-agent path planning on grid maps is a challenging problem and has numerous real-life applications. Running a centralized, systematic search such as A* is complete and cost-optimal but scales up poorly in practice, since both the search space and the branching factor grow exponentially in the number of mobile units. Decentralized approaches, which decompose a problem into several subproblems, can be faster and can work for larger problems. However, existing decentralized methods offer no guarantees with respect to completeness, running time, and solution quality. To address such limitations, we introduce MAPP, a tractable algorithm for multi-agent path planning on grid maps. We show that MAPP has low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. As it runs in low-polynomial time, MAPP is incomplete in the general case. We identify a class of problems for which our algorithm is complete. We believe that this is the first study that formalises restrictions to obtain a tractable class of multi-agent path planning problems. text: http://ijcai.org/papers09/Papers/IJCAI09-310.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:40 PART-8: ROBOTICS and VISION: =311=> 1876p Human Activity Encoding and Recognition Using Low-level Visual Features, Zheshen Wang, Baoxin Li, http://ijcai.org/papers09/Abstracts/311.html Automatic recognition of human activities is among the key capabilities of many intelligent systems with vision/perception. Most existing approaches to this problem require sophisticated feature extraction before classification can be performed. This paper presents a novel approach for human action recognition using only simple low-level visual features: motion captured from direct frame differencing. A codebook of key poses is first created from the training data through unsupervised clustering. Videos of actions are then coded as sequences of super-frames, defined as the key poses augmented with discriminative attributes. A weighted-sequence distance is proposed for comparing two super-frame sequences, which is further wrapped as a kernel embedded in a SVM classifier for the final classification. Compared with conventional methods, our approach provides a flexible non-parametric sequential structure with a corresponding distance measure for human action representation and classification without requiring complex feature extraction. The effectiveness of our approach is demonstrated with the widely-used KTH human activity dataset, for which the proposed method outperforms the existing state-of-the-art. text: http://ijcai.org/papers09/Papers/IJCAI09-311.pdf [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:40 PART-8: ROBOTICS and VISION... [Ответ][Цитата]
Capt.Drew Сообщений: 4179	На: Ai Drew :: IJCAI 09 :: Междунар. ии конфа: Позднее лето-2009 - Коротко о Главном Добавлено: 25 авг 09 5:40 [Ответ][Цитата]
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