nils-herrmann/clean_scirepeval huggingface dataset:
Lazy inference on object identities in wireless sensor networks

Maintaining the identities of moving objects is an important aspect of most multi-object tracking applications. Uncertainty in sensor data, coupled with the intrinsic combinatorial difficulty of the data association problem, suggests probabilistic formulations over the set of possible identities. While an explicit representation of a distribution over all associations may require exponential storage and computation, in practice the information provided by this distribution is accessed only in certain stylized ways, as when asking for the identity of a given track, or the track with a given identity. Exploiting this observation, we proposed a practical solution to this problem based on maintaining marginal probabilities and demonstrated its effectiveness in the context of tracking within a wireless sensor network. That method, unfortunately, requires extensive communication in the network whenever new identity observations are made, in order for normalization operations to keep the marginals consistent. In this paper, we have proposed a very different solution based on accumulated log-likelihoods that can postpone all normalization computations until actual identity queries are made. In this manner the continuous communication and computational expense of repeated normalizations is avoided and that effort is expended only when actual queries are made of the network. We compare the two methods in terms of their computational complexities, inference accuracies, and distributed implementations. Simulation and experimental results from a RFID system are also presented.
Anytime Dynamic A*: An Anytime, Replanning Algorithm

We present a graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion. Our algorithm tunes the quality of its solution based on available search time, at every step reusing previous search efforts. When updated information regarding the underlying graph is received, the algorithm incrementally repairs its previous solution. The result is an approach that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems. We present theoretical analysis of the algorithm, experimental results on a simulated robot kinematic arm, and two current applications in dynamic path planning for outdoor mobile robots.
175 Miles Through the Desert

The DARPA Grand Challenge is one of the biggest open challenges for the robotics community to date. It requires a robotic vehicle to follow a given route of up to 175 miles across punishing desert terrain, without any human supervision. The Challenge was first held in 2004, in which the best performing team failed after 7.3 miles of autonomous driving. The speaker heads one out of 195 teams worldwide competing for the 2 Million Dollar price. Thrun will present the work of the Stanford Racing Team, which is developing an automated car capable of desert driving at up to 50km/h. He will report on research in areas as diverse as computer vision, control, fault-tolerant systems, machine learning, motion planning, data fusion, and 3-D environment modeling.
Autonomous exploration and mapping of abandoned mines

This article discusses the software architecture of an autonomous robotic system designed to explore and map abandoned mines. A new set of software tools is presented, enabling robots to acquire maps of unprecedented size and accuracy. On 30 May 2003, the robot "Groundhog" successfully explored and mapped a main corridor of the abandoned Mathies mine near Courtney, Pennsylvania. This article also discusses some of the challenges that arise in the subterranean environments and some the difficulties of building truly autonomous robots.
Simultaneous localization and mapping with active stereo vision

We present an algorithm for creating globally consistent three-dimensional maps from depth fields produced by camera-based range measurement systems. Our approach is specifically suited to dealing with the high noise levels and the large number of outliers often produced by such systems. Range data is filtered to reject outliers within each scan. The point-to-plane variant of ICP is used for local alignment, including weightings that favor nearby points and a novel outlier rejection strategy that increases the robustness for this class of data while eliminating the burden of user-specified thresholds. Global consistency is imposed on cycles by optimally distributing the cyclic discrepancy according to the local fit correlation matrices. The algorithm is demonstrated on a dataset collected by an active unstructured-light space-time stereo vision system.
Semi-supervised training of models for appearance-based statistical object detection methods

Appearance-based object detection systems using statistical models have proven quite successful. They can reliably detect textured, rigid objects in a variety of poses, lighting conditions and scales. However, the construction of these systems is time-consuming and difficult because a large number of training examples must be collected and manually labeled in order to capture variations in object appearance. Typically, this requires indicating which regions of the image correspond to the object to be detected, and which belong to background clutter, as well as marking key landmark locations on the object. The goal of this work is to pursue and evaluate approaches which reduce the amount of fully labeled examples needed, by training these models in a semi-supervised manner. To this end, we develop approaches based on Expectation-Maximization and self-training that utilize a small number of fully labeled training examples in combination with a set of “weakly labeled” examples. This is advantageous in that weakly labeled data are inherently less costly to generate, since the label information is specified in an uncertain or incomplete fashion. For example, a weakly labeled image might be labeled as containing the training object, with the object location and scale left unspecified. In this work we analyze the performance of the techniques developed through a comprehensive empirical investigation. We find that supplementing a small fully labeled training set with weakly labeled data in the training process reliably improves detector performance for a variety of detection approaches. The outcome is the identification of successful approaches and key issues that are central to achieving good performance in the semi-supervised training of object detection systems.
6D SLAM with an application in autonomous mine mapping

To create with an autonomous mobile robot a 3D volumetric map of a scene it is necessary to gage several 3D scans and to merge them into one consistent 3D model. This paper provides a new solution to the simultaneous localization and mapping (SLAM) problem with six degrees of freedom. Robot motion on natural surfaces has to cope with yaw, pitch and roll angles, turning pose estimation into a problem in six mathematical dimensions. A fast variant of the Iterative Closest Points algorithm registers the 3D scans in a common coordinate system and relocalizes the robot. Finally, consistent 3D maps are generated using a global relaxation. The algorithms have been tested with 3D scans taken in the Mathies mine, Pittsburgh, PA. Abandoned mines pose significant problems to society, yet a large fraction of them lack accurate 3D maps.
Planning with pinch points

We describe a heuristic search algorithm for generating optimal plans in a new class of decision problem, characterized by the incorporation of hidden state. The approach exploits the nature of the hidden state to reduce the state space by orders of magnitude. It then interleaves AO*-type heuristic expansion of the reduced space with forwards and backwards propagation phases to produce a solution in a fraction of the time required by other techniques. Results are provided on an outdoor path planning application.
A passive approach to sensor network localization

Sensor networks present the opportunity to accurately localize the phenomena of interest. To be able to do so however, sensor nodes, need themselves to be accurately localized. We present an algorithm to do this based on uncontrolled environmental sounds observed by each of the sensor nodes. A probabilistic generative model is presented and it is shown that the sensor node localization problem is equivalent to maximum likelihood estimation in the model. Experimental results are presented for both simulated sensor nodes and Crossbow MICA2 sensor nodes.
1 Assisted Media Filtering

Media broadcasts often require personally-identifiable visual information to be obfuscated to preserve anonymity of witnesses, suspects, and minors. Currently, this process requires a manual post-processing step, incurring a significant delay that prevents content from being televised live. Using scale-invariant features and descriptors, we propose a unique clustering system that can automatically obscure an identified target object in subsequent video frames. The system maintains awareness of similar subjects, avoiding obfuscation of incorrect objects while tracking the target.
A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from data, along with the most likely path taken by the robot. Experimental results in cyclic environments of size up to 80 by 25 meter illustrate the appropriateness of the approach.
Large-Scale Robotic 3-D Mapping of Urban Structures

This article present results for building accurate 3-D maps of urban environments with amobile SegwayRMP. The goal of this project is to use robotic systems to rapidly acquire 3-D maps, which seamlessly integrate indoor and outdoor structures. Our approach is based on an information-solution of the SLAM problem, which enables us to seamlessly integrate GPS, IMU, and scan data. 3-D models acquired by the robot are analyzed for navigability using a multi-grid approach, and visualized using a level set technique. Results are presented for a number of environments, some of which combine indoor and outdoor terrain.
A probabilistic approach to inference with limited information in sensor networks

We present a methodology for a sensor network to answer queries with limited and stochastic information using probabilistic techniques. This capability is useful in that it allows sensor networks to answer queries effectively even when present information is partially corrupt and when more information is unavailable or too costly to obtain. We use a Bayesian network to model the sensor network and Markov chain Monte Carlo sampling to perform approximate inference. We demonstrate our technique on the specific problem of determining whether a friendly agent is surrounded by enemy agents and present simulation results for it.
Tractable planning under uncertainty: exploiting structure

The problem of planning under uncertainty has received significant attention in the scientific community over the past few years. It is now well-recognized that considering uncertainty during planning and decision-making is imperative to the design of robust computer systems. This is particularly crucial in robotics, where the ability to interact effectively with real-world environments is a prerequisite for success. 
The Partially Observable Markov Decision Process (POMDP) provides a rich framework for planning under uncertainty. The POMDP model can optimize sequences of actions which are robust to sensor noise, missing information, occlusion, as well as imprecise actuators. While the model is sufficiently rich to address most robotic planning problems, exact solutions are generally intractable for all but the smallest problems. 
This thesis argues that large POMDP problems can be solved by exploiting natural structural constraints. In support of this, we propose two distinct but complementary algorithms which overcome tractability issues in POMDP planning. PBVI is a sample-based approach which approximates a value function solution by planning over a small number of salient information states. PolCA+ is a hierarchical approach which leverages structural properties of a problem to decompose it into a set of smaller, easy-to-solve, problems. These techniques improve the tractability of POMDP planning to the point where POMDP-based robot controllers are a reality. This is demonstrated through the successful deployment of a nursing assistant robot.
MAPLE: Multi-Agent Planning, Learning, and Execution

The fundamental problems addressed in this report pertained to the coordination of multi-agent software systems acting in dynamic, physical environments. The Multi-Agent Planning MAPLE project focused on the development and evaluation of new algorithms for integrating information and for coordinating the actions of large multi-agent teams. Three different aspects of multi-agent systems are in this report. First, this project developed a prototype of a secure decentralized database, which made it possible to disperse information among multiple agents in a secure and scalable way. Second, the project developed a multi-agent system for acquiring and integrating information in large dynamic environments. Third was a planning and multi-agent coordination technique for enabling large teams of autonomous agents to perform missions in highly dynamic and partially observable environments. The resulting algorithms were implemented and evaluated predominately using a simulator called "MapleSim", which simulated the country of Honduras after the devastation caused by Hurricane Mitch in 1998.
Toward a framework for human-robot interaction

The goal of this article is to introduce the reader to the rich and vibrant field of robotics. Robotics is a field in change; the meaning of the term robot today differs substantially from the term just 1 decade ago. The primary purpose of this article is to provide a comprehensive description of past- and present-day robotics. It identifies the major epochs of robotic technology and systems--from industrial to service robotics-and characterizes the different styles of human-robot interaction paradigmatic for each epoch. To help set the agenda for research on human-robot interaction, the article articulates some of the most pressing open questions pertaining to modern-day human-robot interaction.
Real-time fault detection and situational awareness for rovers: report on the Mars technology program task

In this paper we describe the results of a project funded by the Mars technology program at NASA, aimed at developing algorithms to meet this requirement. We describe a number of particle filtering-based algorithms for state estimation which we have demonstrated successfully on diagnosis problems including the K-9 rover at NASA Ames Research Center and the Hyperion rover at CMU. Due to the close interaction between a rover and its environment, traditional discrete approaches to diagnosis are impractical for this domain. Therefore we model rover subsystems as hybrid discrete/continuous systems. There are three major challenges to make particle filters work in this domain. The first is that fault states typically have a very low probability of occurring, so there is a risk that no samples enter fault states. The second issue is coping with the high-dimensional continuous state spaces of the hybrid system models, and the third is the severely constrained computational power available on the rover. This means that very few samples can be used if we wish to track the system state in real time. We describe a number of approaches to rover diagnosis specifically designed to address these challenges.
Tractable Particle Filters for Robot Fault Diagnosis

EXPERIENCE has shown that even carefully designed and tested robots may encounter anomalous situations. It is therefore important for robots to monitor their state so that anomalous situations may be detected in a timely manner. Robot fault diagnosis typically requires tracking a very large number of possible faults in complex non-linear dynamic systems with noisy sensors. Traditional methods either ignore the uncertainly or use linear approximations of nonlinear system dynamics. Such approximations are often unrealistic, and as a result faults either go undetected or become confused with non-fault conditions. Probability theory provides a natural representation for uncertainty, but an exact Bayesian solution for the diagnosis problem is intractable. Monte Carlo approximations have demonstrated considerable success in application domains such as computer vision and robot localization and mapping. But, classical Monte Carlo methods, such as particle filters, can suffer from substantial computational complexity. This is particularly true with the presence of rare, yet important events, such as many system faults. The thesis makes contributions to the theory of probabilistic state-estimation to solve this problem. It presents a novel approach that outperforms existing algorithms (in terms of computational efficiency) for reliably estimating the state of general (nonlinear, nonGaussian) dynamic systems in real-time in the presence of uncertainty (including rare events such as faults). The thesis presents a set of complementary algorithms that provide an approach for computationally tractable fault diagnosis. These algorithms leverage probabilistic approaches to decision theory and information theory to efficiently track a large number of faults in a general dynamic system with noisy measurements. The problem of fault diagnosis is represented as hybrid (discrete/continuous) state estimation. Taking advantage of structure in the domain it dynamically concentrates computation in the regions of state space that are currently most relevant without losing track of less likely states. Experiments with a dynamic simulation of a six-wheel rocker-bogie rover show a significant improvement in performance over the classical approach.
Towards A Framework for Human-Robot Interaction

The goal of this article is to introduce the reader to the rich and vibrant field of robotics, in hopes of laying out an agenda for future research on human robot interaction. Robotics is a field in change; the meaning of the term “robot” today differs substantially from the term just a decade ago. The primary purpose of this article is to provide a comprehensive description of past and present-day robotics. It identifies the major epochs of robotic technology and systems—from industrial to service robotics—and characterizes the different styles of human robot interaction paradigmatic for each epoch. To set an agenda for research on human robot interaction, the article articulates some of the most pressing open questions pertaining to modern-day human robot interaction.
A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots

This paper presents a real-time algorithm for acquiring compact three-dimensional maps of indoor environments, using a mobile robot equipped with range and imaging sensors. Building on previous work on real-time pose estimation during mapping, our approach extends the popular expectation-maximization algorithm to multisurface models, and makes it amenable to real-time execution. Maps acquired by our algorithm consist of compact sets of textured polygons that can be visualized interactively. Experimental results obtained in corridor-type environments illustrate that compact and accurate maps can be acquired in real time and in a fully automated fashion.
A Multi-Resolution Pyramid for Outdoor Robot Terrain Perception

This paper addresses the problem of outdoor terrain modeling for the purposes of mobile robot navigation. We propose an approach in which a robot acquires a set of terrain models at differing resolutions. Our approach addresses one of the major shortcomings of Bayesian reasoning when applied to terrain modeling, namely artifacts that arise from the limited spatial resolution of robot perception. Limited spatial resolution causes small obstacles to be detectable only at close range. Hence, a Bayes filter estimating the state of terrain segments must consider the ranges at which that terrain is observed. We develop a multi-resolution approach that maintains multiple navigation maps, and derive rational arguments for the number of layers and their resolutions. We show that our approach yields significantly better results in a practical robot system, capable of acquiring detailed 3-D maps in large-scale outdoor environments.
Software Architecture of an Autonomous Robotic System

bandoned mines pose significant threats to society,yet a large fraction of them lack accurate maps.This article discusses the software architecture ofan autonomous robotic system designed toexplore and map abandoned mines. A new setof software tools is presented, enabling robots to acquire mapsof unprecedented size and accuracy. On 30 May 2003, ourrobot “Groundhog” successfully explored and mapped a maincorridor of the abandoned Mathies mine near Courtney,Pennsylvania. This article also discusses some of the challengesthat arise in the subterranean environments and some the dif-ficulties of building truly autonomous robots.In recent years, the quest to find and explore new, unex-plored terrain has led to the deployment of more and moresophisticated robotic systems, designed to traverse increasinglyremote locations. Robotic systems have successfully exploredvolcanoes , searched meteorites in Antarctica , [44], tra-versed deserts , explored and mapped the sea bed , andeven explored other planets [26]. This article presents a robotsystem designed to explore spaces much closer to us: aban-doned underground mines.According to a recent survey , “tens of thousands, per-haps even hundreds of thousands, of abandoned mines existtoday in the United States. Not even the U.S. Bureau ofMines knows the exact number, because federal recording ofmining claims was not required until 1976.” Shockingly, weare unaware of the location of many mines; despite the factthat most mines were built just a few generations ago! A
Particle Filters for Rover Fault Diagnosis

states . In addition to the physical state set D, the variable resolution model consists of a set of M abstract states { } ) ( ) 1 ( M a a A = that represent sets of states and/or other abstract states: = ) i ( i ) k ( j a d a A (somewhat simplified) algorithm for abstracting and refining states in the VRPF is shown in  Variable resolution particle filter algorithm.  The initial set of particles { } N 1 i ] i 0 ] i 0 0 x , a B = = are drawn from the prior distribution. R0 is set to the set of unique states (physical or abstract) represented in B0. The particle set, Bt is then recursively drawn from Bt-1 as follows: 1. Project all the particles to physical states to use the physical transition and measurement models. If a particle, 1 t ) j ( ] i [ 1 t B d a − − ∈ = , is in an abstract state, one of its descendant physical states } d { ) j ( , is selected proportional to the prior probability of the physical states as follows: