nils-herrmann/clean_scirepeval huggingface dataset:
Tracking Moving Objects from a Moving, Autonomous Helicopter

In this paper we address the problem of tracking motion in diverse video when the camera may also be in motion. Our implementation handles this diversity by integrating a variety of approaches and selecting the appropriate approach or approaches for the input material using an intelligent algorithm on a frame‐by‐ frame basis. Accuracy is over 75% for some inputs and can handle up to 10 frames per second. 1  Many factors affect the detection and tracking of motion in video. Video capture devices differ in terms of calibration, aspect ratio, resolution, and numerous other factors. Also the capture device could be mounted in different orientations relative to its field of view, could change orientation during the capture process, and/or could move with its platform. The object tracked could be a small or large component of the frame size. It may not have features that can be tracked or it may have comparatively fewer features than its surroundings. Its motion might be constant or could vary with acceleration or present with a non‐ linear path. There might be multiple objects to track. While there has been a reasonable amount of work done on motion‐tracking (see works cited), the problem still remains open and research on the subject is ongoing. Our projected approach, one which combines traditional motion compensation techniques and object tracking techniques with probabilistic models (i.e. particle filters), is a current area of interest. We hope our implementation will yield interesting results. This paper presents our process and results thus far. Our sparse optical flow algorithm consists of feature detection, feature tracking, camera motion compensation, and an intelligent tracking algorithm. While this system achieves good results, we have also implemented an algorithm utilizing dense optical flow methods for comparison and experimentation. Where appropriate, we provide results from both implementations, and realize that our final algorithm may incorporate aspects of both. In this paper we discuss these approaches and provide preliminary qualitative and quantitative results. Section 2 describes previous work. Section 3 presents our approach, formally describing its components and their interaction. Section 4 offers qualitative (video with tracking information superimposed) and quantitative (accuracy and fps metrics) results. Section 5 discusses how the project will progress in the last half of the quarter. Section 6 concludes. 2  The principal problem in tracking motion from moving platforms consists of two sub‐
Planning for Markov Decision Processes with Sparse Stochasticity

Planning algorithms designed for deterministic worlds, such as A* search, usually run much faster than algorithms designed for worlds with uncertain action outcomes, such as value iteration. Real-world planning problems often exhibit uncertainty, which forces us to use the slower algorithms to solve them. Many real-world planning problems exhibit sparse uncertainty: there are long sequences of deterministic actions which accomplish tasks like moving sensor platforms into place, interspersed with a small number of sensing actions which have uncertain outcomes. In this paper we describe a new planning algorithm, called MCP (short for MDP Compression Planning), which combines A* search with value iteration for solving Stochastic Shortest Path problem in MDPs with sparse stochasticity. We present experiments which show that MCP can run substantially faster than competing planners in domains with sparse uncertainty; these experiments are based on a simulation of a ground robot cooperating with a helicopter to fill in a partial map and move to a goal location.
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.
Reconstruction probable de surfaces dans des regions occluses par symetrie calculee

Cette invention concerne un systeme d'obtention d'une carte 3D probable d'une surface occluse d'objet. Le systeme recoit une carte 3D initiale d'une surface visible de l'objet et identifie une ou plusieurs symetries de cette carte 3D initiale. Le systeme calcule la carte 3D probable de la surface occluse par projection de points de la carte 3D initiale dans des regions occluses de l'espace conformement aux symetries identifiees. Le systeme peut egalement comporter un imageur permettant d'obtenir la carte 3D initiale. La surface occluse reelle ne peut certes pas etre connue avec une certitude absolue du fait qu'elle est occluse, mais la carte 3D calculee ressemble etroitement et a maints egards a la surface occluse reelle du fait que la plupart des objets possedent une ou plusieurs symetries et que la carte 3D calculee est fonction de telles symetries identifiees dans la carte 3D initiale de l'objet.
Approximate solutions for partially observable stochastic games with common payoffs

Partially observable decentralized decision making in robot teams is fundamentally different from decision making in fully observable problems. Team members cannot simply apply single-agent solution techniques in parallel. Instead, we must turn to game theoretic frameworks to correctly model the problem. While partially observable stochastic games (POSGs) provide a solution model for decentralized robot teams, this model quickly becomes intractable. We propose an algorithm that approximates POSGs as a series of smaller, related Bayesian games, using heuristics such as QMDP to provide the future discounted value of actions. This algorithm trades off limited look-ahead in uncertainty for computational feasibility, and results in policies that are locally optimal with respect to the selected heuristic. Empirical results are provided for both a simple problem for which the full POSG can also be constructed, as well as more complex, robot-inspired, problems.
Bayesian Landmark Learning for Mobile Robot Localization

To operate successfully in indoor environments, mobile robots must be able to localize themselves. Most current localization algorithms lack flexibility, autonomy, and often optimality, since they rely on a human to determine what aspects of the sensor data to use in localization (e.g., what landmarks to use). This paper describes a learning algorithm, called BaLL, that enables mobile robots to learn what features/landmarks are best suited for localization, and also to train artificial neural networks for extracting them from the sensor data. A rigorous Bayesian analysis of probabilistic localization is presented, which produces a rational argument for evaluating features, for selecting them optimally, and for training the networks that approximate the optimal solution. In a systematic experimental study, BaLL outperforms two other recent approaches to mobile robot localization.
Learning low dimensional predictive representations

Predictive state representations (PSRs) have recently been proposed as an alternative to partially observable Markov decision processes (POMDPs) for representing the state of a dynamical system (Littman et al., 2001). We present a learning algorithm that learns a PSR from observational data. Our algorithm produces a variant of PSRs called transformed predictive state representations (TPSRs). We provide an efficient principal-components-based algorithm for learning a TPSR, and show that TPSRs can perform well in comparison to Hidden Markov Models learned with Baum-Welch in a real world robot tracking task for low dimensional representations and long prediction horizons.
Real-time fault diagnosis [robot fault diagnosis]

This article presents a number of complementary algorithms for detecting faults on-board operating robots, where a fault is defined as a deviation from expected behavior. The algorithms focus on faults that cannot directly be detected from current sensor values but require inference from a sequence of time-varying sensor values. Each algorithm provides an independent improvement over the basic approach. These improvements are not mutually exclusive, and the algorithms may be combined to suit the application domain. All the approaches presented require dynamic models representing the behavior of each of the fault and operational states. These models can be built from analytical models of the robot dynamics, data from simulation, or from the real robot. All the approaches presented detect faults from a finite number of known fault conditions, although there may potentially be a very large number of these faults.
PAO for planning with hidden state

We describe a heuristic search algorithm for generating optimal plans in a new class of decision problem, characterised 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 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.
EM, MCMC, and Chain Flipping for Structure from Motion with Unknown Correspondence

Learning spatial models from sensor data raises the challenging data association problem of relating model parameters to individual measurements. This paper proposes an EM-based algorithm, which solves the model learning and the data association problem in parallel. The algorithm is developed in the context of the the structure from motion problem, which is the problem of estimating a 3D scene model from a collection of image data. To accommodate the spatial constraints in this domain, we compute virtual measurements as sufficient statistics to be used in the M-step. We develop an efficient Markov chain Monte Carlo sampling method called chain flipping, to calculate these statistics in the E-step. Experimental results show that we can solve hard data association problems when learning models of 3D scenes, and that we can do so efficiently. We conjecture that this approach can be applied to a broad range of model learning problems from sensordata, such as the robot mapping problem.
A campaign in autonomous mine mapping

Unknown, unexplored and abandoned subterranean voids threaten mining operations, surface developments and the environment. Hazards within these spaces preclude human access to create and verify extensive maps or to characterize and analyze the environment. To that end, we have developed a mobile robot capable of autonomously exploring and mapping abandoned mines. To operate without communications in a harsh environment with little chance of rescue, this robot must have a robust electro-mechanical platform, a reliable software system, and a dependable means of failure recovery. Presented are the mechanisms, algorithms, and analysis tools that enable autonomous mine exploration and mapping along with extensive experimental results from eight successful deployments into the abandoned Mathies coal mine near Pittsburgh, PA.
Eye Micro-movements Improve Stimulus Detection Beyond the Nyquist Limit in the Peripheral Retina

Even under perfect fixation the human eye is under steady motion (tremor, microsaccades, slow drift). The “dynamic” theory of vision [1, 2] states that eye-movements can improve hyperacuity. According to this theory, eye movements are thought to create variable spatial excitation patterns on the photoreceptor grid, which will allow for better spatiotemporal summation at later stages. We reexamine this theory using a realistic model of the vertebrate retina by comparing responses of a resting and a moving eye. The performance of simulated ganglion cells in a hyperacuity task is evaluated by ideal observer analysis. We find that in the central retina eye-micromovements have no effect on the performance. Here optical blurring limits vernier acuity. In the retinal periphery however, eye-micromovements clearly improve performance. Based on ROC analysis, our predictions are quantitatively testable in electrophysiological and psychophysical experiments.
Learning user models of mobility-related activities through instrumented walking aids

We present a robotic walking aid capable of learning models of users' walking-related activities. Our walker is instrumented to provide guidance to elderly people when navigating their environments; however, such guidance is difficult to provide without knowing what activity a person is engaged in (e.g., where a person wants to go). The main contribution of this paper is an algorithm for learning models of users of the walker. These models are defined at multiple levels of abstractions, and learned from actual usage data using statistical techniques. We demonstrate that our approach succeeds in determining the specific activity in which a user engages when using the walker. One of our proto-type walkers was tested in an assisted living facility near Pittsburgh, PA; a more recent model was extensively evaluated in a university environment.
Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas

The simultaneous localization and mapping (SLAM) with detection and tracking of moving objects (DATMO) problem is not only to solve the SLAM problem in dynamic environments but also to detect and track these dynamic objects. In this paper, we derive the Bayesian formula of the SLAM with DATMO problem, which provides a solid basis for understanding and solving this problem. In addition, we provide a practical algorithm for performing DATMO from a moving platform equipped with range sensors. The probabilistic approach to solve the whole problem has been implemented with the Navlab11 vehicle. More than 100 miles of experiments in crowded urban areas indicated that SLAM with DATMO is indeed feasible.
A Learning Algorithm for Localizing People Based on Wireless Signal Strength that Uses Labeled and Unlabeled Data

This paper summarizes a probabilistic approach for localizing people through the signal strengths of a wireless IEEE 802.1 lb network. Our approach uses data labeled by ground truth position to learn a probabilistic mapping from locations to wireless signals, represented by piecewise linear Gaussians. It then uses sequences of wireless signal data (without position labels) to acquire motion models of individual people, which further improves the localization accuracy. The approach has been implemented and evaluated in an office environment.
An Autonomous Robotic System for Mapping Abandoned Mines

We present the software architecture of a robotic system for mapping abandoned mines. The software is capable of acquiring consistent 2D maps of large mines with many cycles, represented as Markov random fields. 3D C-space maps are acquired from local 3D range scans, which are used to identify navigable paths using A* search. Our system has been deployed in three abandoned mines, two of which inaccessible to people, where it has acquired maps of unprecedented detail and accuracy.
Applying Metric-Trees to Belief-Point POMDPs

Recent developments in grid-based and point-based approximation algorithms for POMDPs have greatly improved the tractability of POMDP planning. These approaches operate on sets of belief points by individually learning a value function for each point. In reality, belief points exist in a highly-structured metric simplex, but current POMDP algorithms do not exploit this property. This paper presents a new metric-tree algorithm which can be used in the context of POMDP planning to sort belief points spatially, and then perform fast value function updates over groups of points. We present results showing that this approach can reduce computation in point-based POMDP algorithms for a wide range of problems.
An Extension of the ICP Algorithm for Modeling Nonrigid Objects with Mobile Robots

The iterative closest point (ICP) algorithm  is a popular method for modeling 3D objects from range data. The classical ICP algorithm rests on a rigid surface assumption. Building on recent work on nonrigid object models [5; 16; 9], this paper presents an ICP algorithm capable of modeling nonrigid objects, where individual scans may be subject to local deformations. We describe an integrated mathematical framework for simultaneously registering scans and recovering the surface configuration. To tackle the resulting high-dimensional optimization problems, we introduce a hierarchical method that first matches a coarse skeleton of scan points, then adapts local scan patches. The approach is implemented for a mobile robot capable of acquiring 3D models of objects.
Policy-contingent abstraction for robust robot control

This paper presents a scalable control algorithm that enables a deployed mobile robot to make high-level control decisions under full consideration of its probabilistic belief. We draw on insights from the rich literature of structured robot controllers and hierarchical MDPs to propose PolCA, a hierarchical probabilistic control algorithm which learns both subtask-specific state abstractions and policies. The resulting controller has been successfully implemented onboard a mobile robotic assistant deployed in a nursing facility. To the best of our knowledge, this work is a unique instance of applying POMDPs to highlevel robotic control problems.
Adapting navigation strategies using motions patterns of people

As people move through their environments, they do not move randomly. Instead, they are often engaged in typical motion patterns, related to specific locations they might be interested in approaching. In this paper we propose a method for adapting the behavior of a mobile robot according to the activities of the people in its surrounding. Our approach uses learned models of people's motion behaviors. Whenever the robot detects a person it computes a probabilistic estimate about which motion pattern the person might be engaged in. During the path planning it then uses this belief to improve its navigation behavior. In different practical experiments carried out on a real robot we demonstrate that our approach allows a robot to quickly adapt its navigation plans according to the activities of the persons in its surrounding. We also present experiments illustrating that our approach provides a better behavior than a standard reactive collision avoidance system.
Point-based value iteration: An anytime algorithm for POMDPs

This paper introduces the Point-Based Value Iteration (PBVI) algorithm for POMDP planning. PBVI approximates an exact value iteration solution by selecting a small set of representative belief points and then tracking the value and its derivative for those points only. By using stochastic trajectories to choose belief points, and by maintaining only one value hyper-plane per point, PBVI successfully solves large problems: we present results on a robotic laser tag problem as well as three test domains from the literature.
Simultaneous localization and mapping with unknown data association using FastSLAM

The extended Kalman filter (EKF) has been the de facto approach to the Simultaneous Localization and Mapping (SLAM) problem for nearly fifteen years. However, the EKF has two serious deficiencies that prevent it from being applied to large, real-world environments: quadratic complexity and sensitivity to failures in data association. FastSLAM, an alternative approach based on the Rao-Blackwellized Particle Filter, has been shown to scale logarithmically with the number of landmarks in the map. This efficiency enables FastSLAM to be applied to environments far larger than could be handled by the EKF. In this paper, we show that FastSLAM also substantially outperforms the EKF in environments with ambiguous data association. The performance of the two algorithms is compared on a real-world data set with various levels of odometric noise. In addition, we show how negative information can be incorporated into FastSLAM in order to improve the accuracy of the estimated map.
Variable Resolution Particle Filter

Particle filters are used extensively for tracking the state of non-linear dynamic systems. This paper presents a new particle filter that maintains samples in the state space at dynamically varying resolution for computational efficiency. Resolution within siatespace varies by region, depending on the belief that the true state lies within each region. Where belief is strong, resolution is fine. Where belief is low, resolution is coarse, abstracting multiple similar states together. The resolution of the statespace is dynamically updated as the belief changes. The proposed algorithm makes an explicit bias-variance tradeoff to select between maintaining samples in a biased generalization of a region of state space versus in a high variance specialization at fine resolution. Samples are maintained at a coarser resolution when the bias introduced by the generalization to a coarse resolution is outweighed by the gain in terms of reduction in variance, and at a finer resolution when it is not. Maintaining samples in abstraction prevents potential hypotheses from being eliminated prematurely for lack of a sufficient number of particles. Empirical results show that our variable resolution particle filter requires significantly lower computation for performance comparable to a classical particle filter.
Learning Occupancy Grid Maps with Forward Sensor Models

This article describes a new algorithm for acquiring occupancy grid maps with mobile robots. Existing occupancy grid mapping algorithms decompose the high-dimensional mapping problem into a collection of one-dimensional problems, where the occupancy of each grid cell is estimated independently. This induces conflicts that may lead to inconsistent maps, even for noise-free sensors. This article shows how to solve the mapping problem in the original, high-dimensional space, thereby maintaining all dependencies between neighboring cells. As a result, maps generated by our approach are often more accurate than those generated using traditional techniques. Our approach relies on a statistical formulation of the mapping problem using forward models. It employs the expectation maximization algorithm for searching maps that maximize the likelihood of the sensor measurements.
Auction Mechanism Design for Multi-Robot Coordination

The design of cooperative multi-robot systems is a highly active research area in robotics. Two lines of research in particular have generated interest: the solution of large, weakly coupled MDPs, and the design and implementation of market architectures. We propose a new algorithm which joins together these two lines of research. For a class of coupled MDPs, our algorithm automatically designs a market architecture which causes a decentralized multi-robot system to converge to a consistent policy. We can show that this policy is the same as the one which would be produced by a particular centralized planning algorithm. We demonstrate the new algorithm on three simulation examples: multi-robot towing, multi-robot path planning with a limited fuel resource, and coordinating behaviors in a game of paint ball.