nils-herrmann/clean_scirepeval huggingface dataset:
Coordination for Multi-Robot Exploration and Mapping

This paper addresses the problem of exploration and mapping of an unknown environment by multiple robots. The mapping algorithm is an on-line approach to likelihood maximization that uses hill climbing to find maps that are maximally consistent with sensor data and odometry. The exploration algorithm explicitly coordinates the robots. It tries to maximize overall utility by minimizing the potential for overlap in information gain amongst the various robots. For both the exploration and mapping algorithms, most of the computations are distributed. The techniques have been tested extensively in real-world trials and simulations. The results demonstrate the performance improvements and robustness that accrue from our multirobot approach to exploration.
Interaction With Mobile Robots in Public Places

Robotics is undergoing a change. While in the past, robots were predominately employed for assembly and transportation purposes in settings like factory floors, a new generation of service robot has begun to emerge, designed to assist people in everyday life [8, 16, 21, 23]. This raises the question as to what physical shape these robots should take, and how they should behave. In mobile robotics, on which this article focuses, these issues remains relatively poorly explored (despite various efforts, such as [1, 6, 11, 15, 30, 31]), specifically in comparison to mobile robot navigation, which has been investigated thoroughly (see e.g., [3, 5, 14]). The goal of developing robots that operate in the same spaces where people live and work raises new questions. What physical appearance should these robots have? What things should they by capable of manipulating, and how? What purposes other than manipulation and navigation should such robots have? How should those robots behave? In which way should they interact with people? This paper focuses on robots operating in public places, where many people interact with a robot for a short time only. Such application domains include receptionists (e.g., in companies, hotels, trade shows), information kiosks, tour-guides, merchandising robots in the retail sector, and so on. The interaction with such robots typically takes place in the short term, that is, people spend only limited time with a robot. This critically limits their ability to adapt to the robot. The interaction with robots in public places is also spontaneous, which precludes extensive preparations on a person’s side (e.g., reading an operation manual). Thus, connecting with modes of interaction people are already familiar with is critical for robots operating in public places. In the context of spontaneous short-term interaction, we conjecture that certain humanoid features can greatly enhance the effectiveness of a robot. In particular, we believe that human-like physical shape and behavior facilitates the communication from robots to people, and also increases a robot’s task effectiveness.
Feature Correspondence: A Markov Chain Monte Carlo Approach

When trying to recover 3D structure from a set of images, the most difficult problem is establishing the correspondence between the measurements. Most existing approaches assume that features can be tracked across frames, whereas methods that exploit rigidity constraints to facilitate matching do so only under restricted camera motion. In this paper we propose a Bayesian approach that avoids the brittleness associated with singling out one "best" correspondence, and instead consider the distribution over all possible correspondences. We treat both a fully Bayesian approach that yields a posterior distribution, and a MAP approach that makes use of EM to maximize this posterior. We show how Markov chain Monte Carlo methods can be used to implement these techniques in practice, and present experimental results on real data.
Efficient Multi-Robot Localization Based on Monte Carlo Approximation

This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot’s belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots, using computer vision and laser range-finding for detecting each other and estimating each other’s relative location. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.
An Online Mapping Algorithm for Teams of Mobile Robots

We propose a new probabilistic algorithm for online mapping of unknown environments with teams of robots. At the core of the algorithm is a technique that combines fast maximum likelihood map growing with a Monte Carlo localizer that uses particle representations. The combination of both yields an online algorithm that can cope with large odometric errors typically found when mapping an environment with cycles. The algorithm can be implemented distributedly on multiple robot platforms, enabling a team of robots to cooperatively generate a single map of their environment. Finally, an extension is described for acquiring three-dimensional maps, which capture the structure and visual appearance of indoor environments in 3D.
A Gesture Based Interface for Human-Robot Interaction

Service robotics is currently a highly active research area in robotics, with enormous societal potential. Since service robots directly interact with people, finding “natural” and easy-to-use user interfaces is of fundamental importance. While past work has predominately focussed on issues such as navigation and manipulation, relatively few robotic systems are equipped with flexible user interfaces that permit controlling the robot by “natural” means. This paper describes a gesture interface for the control of a mobile robot equipped with a manipulator. The interface uses a camera to track a person and recognize gestures involving arm motion. A fast, adaptive tracking algorithm enables the robot to track and follow a person reliably through office environments with changing lighting conditions. Two alternative methods for gesture recognition are compared: a template based approach and a neural network approach. Both are combined with the Viterbi algorithm for the recognition of gestures defined through arm motion (in addition to static arm poses). Results are reported in the context of an interactive clean-up task, where a person guides the robot to specific locations that need to be cleaned and instructs the robot to pick up trash.
Structure from motion without correspondence

A method is presented to recover 3D scene structure and camera motion from multiple images without the need for correspondence information. The problem is framed as finding the maximum likelihood structure and motion given only the 2D measurements, integrating over all possible assignments of 3D features to 2D measurements. This goal is achieved by means of an algorithm which iteratively refines a probability distribution over the set of all correspondence assignments. At each iteration a new structure from motion problem is solved, using as input a set of 'virtual measurements' derived from this probability distribution. The distribution needed can be efficiently obtained by Markov Chain Monte Carlo sampling. The approach is cast within the framework of Expectation-Maximization, which guarantees convergence to a local maximizer of the likelihood. The algorithm works well in practice, as will be demonstrated using results on several real image sequences.
Web interfaces for mobile robots in public places

The growth of the World Wide Web provides unique opportunities to bring robots closer to people. The vision behind such endeavors ranges from relatively simple web-based inspections and surveillance applications to highly versatile applications that use robots connected to the web to establish a remote telepresence in dynamic and populated environments. In the latter scenario, robots play the role of a physical mediator, enabling remote people to acquire information, explore, manipulate, communicate, and interact physically with people far away. The article describes a series of web interfaces designed to remotely operate mobile robots in public places through the web. The design of these interfaces specifically addresses issues such as low bandwidth of interconnections, control brokering, and shared control as well as interaction with people in the robot's environment, which arise naturally in applications with web-based robot control. The interfaces have been tested extensively using two deployed service robots, which were installed as interactive tour guides in two museums. The article also discusses trade-offs and limitations of web-based robots that interact with people in populated public places.
Coordinated deployment of multiple, heterogeneous robots

To be truly useful, mobile robots need to be fairly autonomous and easy to control. This is especially true in situations where multiple robots are used, due to the increase in sensory information and the fact that the robots can interfere with one another. The paper describes a system that integrates autonomous navigation, a task executive, task planning, and an intuitive graphical user interface to control multiple, heterogeneous robots. We have demonstrated a prototype system that plans and coordinates the deployment of teams of robots. Testing has shown the effectiveness and robustness of the system, and of the coordination strategies in particular.
Probabilistic Algorithms in Robotics

This article describes a methodology for programming robots known as probabilistic robotics. The probabilistic paradigm pays tribute to the inherent uncertainty in robot perception, relying on explicit representations of uncertainty when determining what to do. This article surveys some of the progress in the field, using in-depth examples to illustrate some of the nuts and bolts of the basic approach. My central conjecture is that the probabilistic approach to robotics scales better to complex real-world applications than approaches that ignore a robot's uncertainty.
Collaborative multi-robot exploration

In this paper we consider the problem of exploring an unknown environment by a team of robots. As in single-robot exploration the goal is to minimize the overall exploration time. The key problem to be solved therefore is to choose appropriate target points for the individual robots so that they simultaneously explore different regions of their environment. We present a probabilistic approach for the coordination of multiple robots which, in contrast to previous approaches, simultaneously takes into account the costs of reaching a target point and the utility of target points. The utility of target points is given by the size of the unexplored area that a robot can cover with its sensors upon reaching a target position. Whenever a target point is assigned to a specific robot, the utility of the unexplored area visible from this target position is reduced for the other robots. This way, a team of multiple robots assigns different target points to the individual robots. The technique has been implemented and tested extensively in real-world experiments and simulation runs. The results given in this paper demonstrate that our coordination technique significantly reduces the exploration time compared to previous approaches.
Towards programming tools for robots that integrate probabilistic computation and learning

This paper describes a programming language extension of C++, called CES, specifically targeted towards mobile robot control. CES's design is motivated by a recent series of successful probabilistic methods for mobile robot control, with the goal of facilitating the development of such probabilistic software in future robot applications. CES extends C++ by two ideas: Computing with probability distributions, and built-in mechanisms for learning from examples as a new means of programming. An example program, used to control a mail-delivering robot with gesture command interface, illustrates that CES may reduce the code development by two orders of magnitude. CES differs from other special-purpose programming languages in the field, which typically emphasize concurrency and real-time/event-driven processing.
A real-time algorithm for mobile robot mapping with applications to multi-robot and 3D mapping

We present an incremental method for concurrent mapping and localization for mobile robots equipped with 2D laser range finders. The approach uses a fast implementation of scan-matching for mapping, paired with a sample-based probabilistic method for localization. Compact 3D maps are generated using a multi-resolution approach adopted from the computer graphics literature, fed by data from a dual laser system. Our approach builds 3D maps of large, cyclic environments in real-time, and it is robust. Experimental results illustrate that accurate maps of large, cyclic environments can be generated even in the absence of any odometric data.
A Probabilistic Approach to Collaborative Multi-Robot Localization

This paper presents a statistical algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The technique has been implemented and tested using two mobile robots equipped with cameras and laser range-finders for detecting other robots. The results, obtained with the real robots and in series of simulation runs, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization. A further experiment demonstrates that under certain conditions, successful localization is only possible if teams of heterogeneous robots collaborate during localization.
A Programming Language Extension for Probabilistic Robot Programming

In recent years, probabilistic techniques have led to improved solutions for many robotics problems. However, no general tools are currently available to aid the development of probabilistic robotic software. This paper presents a programming language extension to C++ that integrates probabilistic computation and learning. Its two main ideas are to make probability distributions as usable as floating-point numbers, and to smoothly integrate function approximators into C++ code. These innovations facilitate the development of robust, probabilistic robot software, as illustrated by a proto-type program for a mail delivery robot.
Collaborative Multi-Robot Localization

This paper presents a probabilistic algorithm for collaborative mobile robot localization. Our approach uses a sample-based version of Markov localization, capable of localizing mobile robots in an any-time fashion. When teams of robots localize themselves in the same environment, probabilistic methods are employed to synchronize each robot's belief whenever one robot detects another. As a result, the robots localize themselves faster, maintain higher accuracy, and high-cost sensors are amortized across multiple robot platforms. The paper also describes experimental results obtained using two mobile robots. The robots detect each other and estimate their relative locations based on computer vision and laser range-finding. The results, obtained in an indoor office environment, illustrate drastic improvements in localization speed and accuracy when compared to conventional single-robot localization.
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes

We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using samples, along with density trees generated from such samples. A Monte Carlo version of Baum-Welch (EM) is employed to learn models from data. Regularization during learning is achieved using an exponential shrinking technique. The shrinkage factor, which determines the effective capacity of the learning algorithm, is annealed down over multiple iterations of Baum-Welch, and early stopping is applied to select the right model. Once trained, Monte Carlo HMMs can be run in an any-time fashion. We prove that under mild assumptions, Monte Carlo Hidden Markov Models converge to a local maximum in likelihood space, just like conventional HMMs. In addition, we provide empirical results obtained in a gesture recognition domain.
Using the CONDENSATION algorithm for robust, vision-based mobile robot localization

To navigate reliably in indoor environments, a mobile robot must know where it is. This includes both the ability of globally localizing the robot from scratch, as well as tracking the robot's position once its location is known. Vision has long been advertised as providing a solution to these problems, but we still lack efficient solutions in unmodified environments. Many existing approaches require modification of the environment to function properly, and those that work within unmodified environments seldomly address the problem of global localization. In this paper we present a novel, vision-based localization method based on the CONDENSATION algorithm, a Bayesian filtering method that uses a sampling-based density representation. We show how the CONDENSATION algorithm can be rued in a novel way to track the position of the camera platform rather than tracking an object in the scene. In addition, it can also be used to globally localize the camera platform, given a visual map of the environment. Based on these two observations, we present a vision-based robot localization method that provides a solution to a difficult and open problem in the mobile robotics community. As evidence for the viability of our approach, we show both global localization and tracking results in the context of a state of the art robotics application.
Sonar-Based Mapping With Mobile Robots Using EM

This paper presents an algorithms for learning occupancy grid maps with mobile robots equipped with range finders, such as sonar sensors. Our approach employs the EM algorithm to solve the concurrent mapping and localization problem. To accommodate the spatial nature of range data, it relies on a two-layered representation of maps, where global maps are composed from a collection of small, local maps. To avoid local minima during likelihood maximization, a softmax version of the M step is proposed that is gradually annealed to the exact maximum. Experimental results demonstrate that our approach is well suited for constructing large maps of typical indoor environments using sensors as inaccurate as sonars.
When Robots Meet People: Research Directions In Mobile Robotics

Recent research in the field of mobile robotics has led to significant progress along various dimensions. Applications such as robots that guide blind or mentally handicapped people, robots that clean large office buildings and department stores, robots that assist people in recreational activities, etc., are clearly in reach, and for many of those target domains prototypes are readily available. This recent, ongoing revolution has been triggered by advances along various dimensions. Robotic hardware has steadily become cheaper and more reliable. Robotic software has matured, reaching critical levels of reliability, robustness, and flexibility. Mobile robots have an enormous potential to change our everyday lives. It is worth noting that an increasing fraction of these robots rely on methods developed in artificial intelligence. Together with researchers at the University of Bonn (W. Burgard, A.B. Cremers, D. Fox, D. Hahnel, G. Lakemeyer, D. Schultz and W. Steiner), and motivated in part by the work of Horswill , the author recently installed a mobile tourguide robot in a branch of the Deutsches Museum [1, 4]. The robot’s task was to guide visitors through the museum, explaining to them a subset of the museum’s exhibits. The major challenges that arose in this project can be grouped into two categories: navigation and human robot interaction.
Monte Carlo Localization: Efficient Position Estimation for Mobile Robots

This paper presents a new algorithm for mobile robot localization, called Monte Carlo Localization (MCL). MCL is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success. However, previous approaches were either computationally cumbersome (such as grid-based approaches that represent the state space by high-resolution 3D grids), or had to resort to extremely coarse-grained resolutions. Our approach is computationally efficient while retaining the ability to represent (almost) arbitrary distributions. MCL applies sampling-based methods for approximating probability distributions, in a way that places computation "where needed." The number of samples is adapted on-line, thereby invoking large sample sets only when necessary. Empirical results illustrate that MCL yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches. It is also much easier to implement.
Issues in Using Function Approximation for Reinforcement Learning

Reinforcement learning techniques address the problem of learning to select actions in unknown, dynamic environments. It is widely acknowledged that to be of use in complex domains, reinforcement learning techniques must be combined with generalizing function approximation methods such as artificial neural networks. Little, however, is understood about the theoretical properties of such combinations, and many researchers have encountered failures in practice. In this paper we identify a prime source of such failures—namely, a systematic overestimation of utility values. Using Watkins’ Q-Learning [18] as an example, we give a theoretical account of the phenomenon, deriving conditions under which one may expected it to cause learning to fail. Employing some of the most popular function approximators, we present experimental results which support the theoretical findings.
Planning with an Adaptive W Orld Model

We present a new connectionist planning method [TML90]. By interaction with an unknown environment, a world model is progressively constructed using gradient descent. For deriving optimal actions with respect to future reinforcement, planning is applied in two steps: an experience network proposes a plan which is subsequently optimized by gradient descent with a chain of world models, so that an optimal reinforcement may be obtained when it is actually run. The appropriateness of this method is demonstrated by a robotics application and a pole balancing task.
Probabilistic Methods for State Estimation in Robotics

The eld of Arti cial Intelligence (AI) is currently undergoing a transition. While in the eighties, rule-based and logical representations were the representation of choice in the majority of AI systems, in recent years various researchers have explored alternative representational frameworks, which emphasis on frameworks that enable systems to represent and handle uncertainty. Out of those, probabilistic methods (and speci cally Bayesian methods) have probably been analyzed most thoroughly and applied most successfully in a variety of problem domains.