nils-herrmann/clean_scirepeval huggingface dataset:
Coastal Navigation with Mobile Robots

The problem that we address in this paper is how a mobile robot can plan in order to arrive at its goal with minimum uncertainty. Traditional motion planning algorithms often assume that a mobile robot can track its position reliably, however, in real world situations, reliable localization may not always be feasible. Partially Observable Markov Decision Processes (POMDPs) provide one way to maximize the certainty of reaching the goal state, but at the cost of computational intractability for large state spaces. 
 
The method we propose explicitly models the uncertainty of the robot's position as a state variable, and generates trajectories through the augmented pose-uncertainty space. By minimizing the positional uncertainty at the goal, the robot reduces the likelihood it becomes lost. We demonstrate experimentally that coastal navigation reduces the uncertainty at the goal, especially with degraded localization.
MINERVA: a second-generation museum tour-guide robot

This paper describes an interactive tour-guide robot, which was successfully exhibited in a Smithsonian museum. During its two weeks of operation, the robot interacted with thousands of people, traversing more than 44 km at speeds of up to 163 cm/sec. Our approach specifically addresses issues such as safe navigation in unmodified and dynamic environments, and short-term human-robot interaction. It uses learning pervasively at all levels of the software architecture.
Coastal Navigation { Robot Motion with Uncertainty

Ships often use the coasts of continents for navigation in the absence of better tools such as GPS, since being close to the land allows sailors to determine with high accuracy where they are. Similarly for mobile robots, in many environments global and accurate localization is not always feasible. Environments can lack features, and dynamic obstacles such as people can confuse and block sensors. In this paper, we demonstrate a technique for achieving better localization by generating trajectories that model explicitly both the information content of the environment, and the density of the people in the environment. These trajectories reduce the average positional certainty along their length, reducing the likelihood the robot will become lost at any point.
Learning to Rendezvous during Multi-agent Explo- ration

We consider the problem of rendezvous between two robots collaborating in learning the layout of an unknown environment. That is, how can two autonomous exploring agents that cannot communicate with one another over long distances meet if they start exploring at di erent locations in an unknown environment. The intended application is collaborative map exploration. Ours is the rst work to formalize the characteristics of the rendezvous problem, and we approach it by proposing several alternative algorithms that the robots could use in attempting to rendezvous quickly while continuing to explore. The algorithms are based on the assumption that potential rendezvous locations, referred to as landmarks, can be determined by the robots as they explore; these locations are based on a distinctiveness measure computed with an arbitrary sensor. We consider the performance of our proposed algorithms analytically with respect to both expectedand worst-case behaviour. We then examine their behaviour under a wider set of conditions in
Autonomous Exploration: An Integrated Systems Approach

Mobile robotic systems offer an ideal platform for testing and implementing many of the concepts developed in more abstract artificial intelligence. Robotic systems embody a complex interaction of computation, perception and actuation that depend upon such familiar tasks as recognition and reaction. In order for robots to perform real-world tasks such as navigation, localization and exploration, th e subsystems of motion, sensing and computation must be merged into a single, realizable unit that uses the differen t techniques together. Our group is investigating problems in the domain of computational perception, in the context of mobile robotic s. In particular, we are concerned with environment exploration, and map construction. We are using the AAAI 1997 Mobile Robot competition as an opportunity to test a number of implementations of systems in navigation, spatial reasoning and perception.
Learning to Rendezvous during Multi-agent

We consider the problem of rendezvous between two robots collaborating in learning the layout of an unknown environment. That is, how can two autonomous exploring agents that cannot communicate with one another over long distances meet if they start exploring at diierent locations in an unknown environment. The intended application is collaborative map exploration. Ours is the rst work to formalize the characteristics of the rendezvous problem, and we approach it by proposing several alternative algorithms that the robots could use in attempting to rendezvous quickly while continuing to explore. The algorithms are based on the assumption that potential rendezvous locations, referred to as landmarks, can be determined by the robots as they explore; these locations are based on a distinctiveness measure computed with an arbitrary sensor. We consider the performance of our proposed algorithms analytically with respect to both expected-and worst-case behaviour. We then examine their behaviour under a wider set of conditions in simulation.
Mobile Robot Navigation and Control: A Case Study

Robotic systems (and in particular mobile autonomous agents) embody a complex interaction of computational processes, mechanical systems, sensors, and communications hardware. System integration can present significant difficulties to the construction of a real system, because the hardware is often built around convenience of design rather than convenience of system integration. Nonetheless, in order for robots to perform real-world tasks such as navigation, localization and exploration, the different subsystems of motion, sensing and computation must be merged into a single, realisable unit. Our group is investigating particular problems in the domain of computational perception, in the context of mobile robotics. In particular, we are concerned with environment exploration, position estimation, and map construction. We have several mobile platforms integrating different sensing modalities, which we are able to control simultaneously from a single source.
Surface sensing and classification for efficient mobile robot navigation

Mobile robot navigation and localization is frequently aided by, or even dependent upon, a good estimate of the rate of dead-reckoning error accumulation. Sensor data can be used for position estimation, but this often involves overheads in acquiring and processing the data. By sensing and then classifying the surface type, an estimate of the rate of error accumulation for dead-reckoning allows one to estimate accurately how often localization, including sensor data acquisition, must be performed. The authors describe experiments in which a boom-mounted microphone is tapped on different floor materials, much as a blind man might tap his cane. The acoustic signature arising from the contact is then used to classify the floor type by comparing a windowed power spectrum of the acoustic signature with one of a family of prototypical signatures generated statistically from the same material. The technique is low-cost, involves limited computational expense, and performs very well.
Spectral Bounds for Sparse PCA: Exact and Greedy Algorithms

Sparse PCA seeks approximate sparse "eigenvectors" whose projections capture the maximal variance of data. As a cardinality-constrained and non-convex optimization problem, it is NP-hard and is encountered in a wide range of applied fields, from bio-informatics to finance. Recent progress has focused mainly on continuous approximation and convex relaxation of the hard cardinality constraint. In contrast, we consider an alternative discrete spectral formulation based on variational eigenvalue bounds and provide an effective greedy strategy as well as provably optimal solutions using branch-and-bound search. Moreover, the exact methodology used reveals a simple renormalization step that improves approximate solutions obtained by any continuous method. The resulting performance gain of discrete algorithms is demonstrated on real-world benchmark data and in extensive Monte Carlo evaluation trials.
Noise and the two-thirds power Law

The two-thirds power law, an empirical law stating an inverse non-linear relationship between the tangential hand speed and the curvature of its trajectory during curved motion, is widely acknowledged to be an invariant of upper-limb movement. It has also been shown to exist in eye-motion, locomotion and was even demonstrated in motion perception and prediction. This ubiquity has fostered various attempts to uncover the origins of this empirical relationship. In these it was generally attributed either to smoothness in hand- or joint-space or to the result of mechanisms that damp noise inherent in the motor system to produce the smooth trajectories evident in healthy human motion. 
 
We show here that white Gaussian noise also obeys this power-law. Analysis of signal and noise combinations shows that trajectories that were synthetically created not to comply with the power-law are transformed to power-law compliant ones after combination with low levels of noise. Furthermore, there exist colored noise types that drive non-power-law trajectories to power-law compliance and are not affected by smoothing. These results suggest caution when running experiments aimed at verifying the power-law or assuming its underlying existence without proper analysis of the noise. Our results could also suggest that the power-law might be derived not from smoothness or smoothness-inducing mechanisms operating on the noise inherent in our motor system but rather from the correlated noise which is inherent in this motor system.
Incorporating Constraints and Prior Knowledge into Factorization Algorithms - An Application to 3D Recovery

Matrix factorization is a fundamental building block in many computer vision and machine learning algorithms. In this work we focus on the problem of structure from motion in which one wishes to recover the camera motion and the 3D coordinates of certain points given their 2D locations. This problem may be reduced to a low rank factorization problem. When all the 2D locations are known, singular value decomposition yields a least squares factorization of the measurements matrix. In realistic scenarios this assumption does not hold: some of the data is missing, the measurements have correlated noise, and the scene may contain multiple objects. Under these conditions, most existing factorization algorithms fail while human perception is relatively unchanged. In this work we present an EM algorithm for matrix factorization that takes advantage of prior information and imposes strict constraints on the resulting matrix factors. We present results on challenging sequences.
Globally optimal solutions for energy minimization in stereo vision using reweighted belief propagation

A wide range of low level vision problems have been formulated in terms of finding the most probable assignment of a Markov random field (or equivalently the lowest energy configuration). Perhaps the most successful example is stereo vision. For the stereo problem, it has been shown that finding the global optimum is NP hard but good results have been obtained using a number of approximate optimization algorithms. In this paper, we show that for standard benchmark stereo pairs, the global optimum can be found in about 30 minutes using a variant of the belief propagation (BP) algorithm. We extend previous theoretical results on reweighted belief propagation to account for possible ties in the beliefs and using these results we obtain easily checkable conditions that guarantee that the BP disparities are the global optima. We verify experimentally that these conditions are typically met for the standard benchmark stereo pairs and discuss the implications of our results for further progress in stereo.
Multibody factorization with uncertainty and missing data using the EM algorithm

Multibody factorization algorithms give an elegant and simple solution to the problem of structure from motion even for scenes containing multiple independent motions. Despite this elegance, it is still quite difficult to apply these algorithms to arbitrary scenes. First, their performance deteriorates rapidly with increasing noise. Second, they cannot be applied unless all the points can be tracked in all the frames (as will rarely happen in real scenes). Third, they cannot incorporate prior knowledge on the structure or the motion of the objects. In this paper we present a multibody factorization algorithm that can handle arbitrary noise covariance for each feature as well as missing data. We show how to formulate the problem as one of factor analysis and derive an expectation-maximization based maximum-likelihood algorithm. One of the advantages of our formulation is that we can easily incorporate prior knowledge, including the assumption of temporal coherence. We show that this assumption greatly enhances the robustness of our algorithm and present results on challenging sequences.
Colorization using optimization

Colorization is a computer-assisted process of adding color to a monochrome image or movie. The process typically involves segmenting images into regions and tracking these regions across image sequences. Neither of these tasks can be performed reliably in practice; consequently, colorization requires considerable user intervention and remains a tedious, time-consuming, and expensive task.In this paper we present a simple colorization method that requires neither precise image segmentation, nor accurate region tracking. Our method is based on a simple premise; neighboring pixels in space-time that have similar intensities should have similar colors. We formalize this premise using a quadratic cost function and obtain an optimization problem that can be solved efficiently using standard techniques. In our approach an artist only needs to annotate the image with a few color scribbles, and the indicated colors are automatically propagated in both space and time to produce a fully colorized image or sequence. We demonstrate that high quality colorizations of stills and movie clips may be obtained from a relatively modest amount of user input.
Learning object detection from a small number of examples: the importance of good features

Face detection systems have recently achieved high detection rates and real-time performance. However, these methods usually rely on a huge training database (around 5,000 positive examples for good performance). While such huge databases may be feasible for building a system that detects a single object, it is obviously problematic for scenarios where multiple objects (or multiple views of a single object) need to be detected. Indeed, even for multi-viewface detection the performance of existing systems is far from satisfactory. In this work we focus on the problem of learning to detect objects from a small training database. We show that performance depends crucially on the features that are used to represent the objects. Specifically, we show that using local edge orientation histograms (EOH) as features can significantly improve performance compared to the standard linear features used in existing systems. For frontal faces, local orientation histograms enable state of the art performance using only a few hundred training examples. For profile view faces, local orientation histograms enable learning a system that seems to outperform the state of the art in real-time systems even with a small number of training examples.
Generalized belief propagation receiver for near-optimal detection of two-dimensional channels with memory

We propose a generalized belief propagation (GBP) receiver for two-dimensional (2D) channels with memory, which is applicative to 2D intersymbol interference (ISI) equalization and multiuser detection (MUD). Our experimental study demonstrates that under non-trivial interference conditions, the performance of this fully tractable GBP receiver is almost identical to the performance of the optimal maximum a-posteriori (MAP) receiver.
Learning From a Small Number of Training Examples by Exploiting Object Categories

In the last few years, object detection techniques have progressed immensely. Impressive detection results have been achieved for many objects such as faces [11, 14, 9] and cars [11]. The robustness of these systems emerges from a training stage utilizing thousands of positive examples. One approach to enable learning from a small set of training examples is to find an efficient set of features that accurately represent the target object. Unfortunately, automatically selecting such a feature set is a difficult task in itself. In this paper we present a novel feature selection method that is based on the notion of object categories. We assume that when learning to recognize a new object (like an apple) we also know a category it belongs to (fruit). We further assume that features that are useful for learning other objects in the same category (e.g. pear or orange) will also be useful for learning the novel object. This leads to a simple criterion for selecting features and building classifiers. We show that our method gives significant improvement in detection performance in challenging domains.
Separating reflections from a single image using local features

When we take a picture through a window, the image we obtain is often a linear superposition of two images: the image of the scene beyond the window plus the image of the scene reflected by the window. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there is an infinite number of valid decompositions. We describe an algorithm that uses an extremely simple form of prior knowledge to perform the decomposition. Given a single image as input, the algorithm searches for a decomposition into two images that minimize the total amount of edges and comers. The search is performed using belief propagation on a patch representation of the image. We show that this simple prior is surprisingly powerful: our algorithm obtains "correct" separations on challenging reflection scenes using only a single image.
User Assisted Separation of Reflections from a Single Image Using a Sparsity Prior

When we take a picture through transparent glass the image we obtain is often a linear superposition of two images: the image of the scene beyond the glass plus the image of the scene reflected by the glass. Decomposing the single input image into two images is a massively ill-posed problem: in the absence of additional knowledge about the scene being viewed there are an infinite number of valid decompositions. In this paper we focus on an easier problem: user assisted separation in which the user interactively labels a small number of gradients as belonging to one of the layers. Even given labels on part of the gradients, the problem is still ill-posed and additional prior knowledge is needed. Following recent results on the statistics of natural images we use a sparsity prior over derivative filters. This sparsity prior is optimized using the terative reweighted least squares (IRLS) approach. Our results show that using a prior derived from the statistics of natural images gives a far superior performance compared to a Gaussian prior and it enables good separations from a modest number of labeled gradients.
Learning and inferring image segmentations using the GBP typical cut algorithm

Significant progress in image segmentation has been made by viewing the problem in the framework of graph partitioning. In particular, spectral clustering methods such as "normalized cuts" (ncuts) can efficiently calculate good segmentations using eigenvector calculations. However, spectral methods when applied to images with local connectivity often oversegment homogenous regions. More importantly, they lack a straightforward probabilistic interpretation which makes it difficult to automatically set parameters using training data. In this paper we revisit the typical cut criterion proposed by Blatt et al. (1997) and Gdalyahu et al (2001). We show that computing the typical cut is equivalent to performing inference in an undirected graphical model. This equivalence allows us to use the powerful machinery of graphical models for learning and inferring image segmentations. For inferring segmentations we show that the generalized belief propagation (GBP) algorithm can give excellent results with a runtime that is usually faster than the ncut eigensolver. For learning segmentations we derive a maximum likelihood learning algorithm to learn affinity matrices from labelled datasets. We illustrate both learning and inference on challenging real and synthetic images.
Learning how to inpaint from global image statistics

Inpainting is the problem of filling-in holes in images. Considerable progress has been made by techniques that use the immediate boundary of the hole and some prior information on images to solve this problem. These algorithms successfully solve the local inpainting problem but they must, by definition, give the same completion to any two holes that have the same boundary, even when the rest of the image is vastly different. We address a different, more global inpainting problem. How can we use the rest of the image in order to learn how to inpaint? We approach this problem from the context of statistical learning. Given a training image we build an exponential family distribution over images that is based on the histograms of local features. We then use this image specific distribution to inpaint the hole by finding the most probable image given the boundary and the distribution. The optimization is done using loopy belief propagation. We show that our method can successfully complete holes while taking into account the specific image statistics. In particular it can give vastly different completions even when the local neighborhoods are identical.
Factorization with Uncertainty and Missing Data: Exploiting Temporal Coherence

The problem of "Structure From Motion" is a central problem in vision: given the 2D locations of certain points we wish to recover the camera motion and the 3D coordinates of the points. Under simplified camera models, the problem reduces to factorizing a measurement matrix into the product of two low rank matrices. Each element of the measurement matrix contains the position of a point in a particular image. When all elements are observed, the problem can be solved trivially using SVD, but in any realistic situation many elements of the matrix are missing and the ones that are observed have a different directional uncertainty. Under these conditions, most existing factorization algorithms fail while human perception is relatively unchanged. 
 
In this paper we use the well known EM algorithm for factor analysis to perform factorization. This allows us to easily handle missing data and measurement uncertainty and more importantly allows us to place a prior on the temporal trajectory of the latent variables (the camera position). We show that incorporating this prior gives a significant improvement in performance in challenging image sequences.