nils-herrmann/clean_scirepeval huggingface dataset:
A neural-network based approach for recognition of pose and motion gestures on a mobile robot

Since a variety of changes in both robotic hardware and software suggests that service robots will soon become possible, to find "natural" ways of communication between human and robots is of fundamental importance for the robotic field. The paper describes a gesture-based interface for human-robot interaction, which enables people to instruct robots through easy-to-perform arm gestures. Such gestures might be static pose gestures, which involve only a specific configuration of the person's arm, or they might be dynamic motion gestures, that is, they involve motion (such as waving). Gestures are recognized in real-time at approximate frame rate, using neural networks. A fast, color-based tracking algorithm enables the robot to track and follow a person reliably through office environments with drastically changing lighting conditions. 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 the robot picks up trash which it then delivers to the nearest trash-bin.
Super-resolved texture tracking of planar surface patches

We present an approach to tracking planar surface patches over time. In addition to tracking a patch with full six degrees of freedom, the algorithm also produces a super-resolved estimate of the texture present on the patch. This texture estimate is kept as an explicit model texture image which is refined over time. We then use it to infer the 3D motion of the patch from the image sequence. The main idea behind the approach is to use a technique from computer graphics, known as texture mapping, as the measurement model in an extended Kalman filter. We also calculate the partial derivative of this image formation process with respect to the 3D pose of the patch, which functions as the measurement Jacobian. The super-resolved estimate of the texture is obtained using the standard extended Kalman filter measurement update, with one essential approximation that makes this computationally feasible. The resulting equations are remarkably simple, yet lead to estimates that are properly super-resolved. In addition to developing the theory behind the approach, we also demonstrate both the tracking and the super-resolution aspect of the algorithm on real image sequences.
Coastal Navigation { Robot Motion with

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 trajec-tories 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 Learn

Preface. Part I: Overview Articles. 1. Learning to Learn: Introduction and Overview S. Thrun, L. Pratt. 2. A Survey of Connectionist Network Reuse Through Transfer L. Pratt, B. Jennings. 3. Transfer in Cognition A. Robins. Part II: Prediction. 4. Theoretical Models of Learning to Learn J. Baxter. 5. Multitask Learning R. Caruana. 6. Making a Low-Dimensional Representation Suitable for Diverse Tasks N. Intrator, S. Edelman. 7. The Canonical Distortion Measure for Vector Quantization and Function Approximation J. Baxter. 8. Lifelong Learning Algorithms S. Thrun. Part III: Relatedness. 9. The Parallel Transfer of Task Knowledge Using Dynamic Learning Rates Based on a Measure of Relatedness D.L. Silver, R.E. Mercer.  Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge S. Thrun,  A First Step Towards Continual Learning M.B. Ring. 12. Reinforcement Learning with Self-Modifying Policies J. Schmidhuber, et al. 13. Creating Advice-Taking Reinforcement Learners R. Maclin, J.W. Shavlik.
A hybrid collision avoidance method for mobile robots

Proposes a hybrid approach to the problem of collision avoidance for indoor mobile robots. The /spl mu/DWA (model-based dynamic window approach) integrates sensor data from various sensors with information extracted from a map of the environment, to generate collision-free motion. A novel integration rule ensures that with high likelihood, the robot avoids collisions with obstacles not detectable with its sensors, even if it is uncertain about its position. The approach was implemented and tested extensively as part of an installation, in which a mobile robot gave interactive tours to visitors of the "Deutsches Museum Bonn." Here our approach was essential for the success of the entire mission, because a large number of ill-shaped obstacles prohibited the use of purely sensor-based methods for collision avoidance.
The Museum Tourguide Project: Experiences with a Deployed Service Robot

The field of robotics is currently undergoing a major change. In the next decade, we will witness the deployment of a rapidly increasing number of service robots, i.e., robots that will directly provide assistance to people in their daily activities. The new application domains pose new challenges for the field of robotics. There is a need for more flexible and robust methods for robot control, along with more natural interactive user interfaces. This talk describes RHINO, a mobile robot that has recently been deployed at the “Deutsches Museum” as an interactive tourguide. RHINO interacts with and provides guided tours to visitors of the museum. Remote users can also monitor and control RHINO’s operation through a Webbased multi-modal interface. This talk will discuss RHINO’s software architecture, which integrates various modules for high-speed navigation, map learning, and user interaction. Empirical results will also be presented, along with lessons that were learned when deploying the robot in the museum. The work has been carried out jointly with W. Burgard, D. Fox, D. Haehnel, D. Schulz, and W. Steiner from the University of Bonn. O-8186-8138-1/97 $10.00
Explanation-based learning for mobile-robot perception

Explanation-based neural network learning (EBNN) has recently been proposed as a method for reducing the amount of training data required for reliable generalization, by relying instead on approximate, previously learned knowledge. We present first experiments applying EBNN to the problem of learning object recognition for a mobile robot. In these experiments, a mobile robot traveling down a hallway corridor learns to recognize distant doors based on color camera images and sonar sensations. The previously learned knowledge corresponds to a neural network that recognizes nearby doors, and a second network that predicts the state of the world after travelling forward in the corridor. Experimental results show that EBNN is able to use this approximate prior knowledge to significantly reduce the number of training examples required to learn to recognize distant doors. We also present results of experiments in which networks learned by EBNN (e.g., "there is a door 2 meters ahead") are then used as background knowledge for learning subsequent functions (e.g., "there is a door 3 meters ahead").
A Probabilistic Approach for Concurrent Map Acquisition 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.
Discovering Landmarks for Mobile Robot Navigation

Positioning addresses the problem of determining the position of a mobile robot from sensor data. This paper presents a method with which a mobile robot can automatically discover a set of landmarks used as reference for positioning,and also train artificial neural networks to re cognize them. The method is applicable to a large variety of sensors and environments. Extensive experiments with a mobile robot equipped with sonar sensors and a camera, illustrate that landmarks selected by the robot are superio r to landmarks carefully selected by human experts. Thus, the new method has two advantages over previous ones: It improves the ability of a robot to position itself, and it eliminates the burden of having to select appropriate landmarks by hand.
To Know or Not to Know: On the Utility of Models in Mobile Robotics

—This article describes Jeeves, one of the winning entries in the 1996 AAAI mobile robot competition. Jeeves tied for first place in the finals of the competition, after it won both preliminary trials. A key aspect in Jeeves’s software design was the ability to acquire a model of the environment. The model, a geometric map constructed from sensory data while the robot performs its task, enabled Jeeves to sweep the arena efficiently. It facilitated the retrieval of balls and their delivery at the gate, and it helped to avoid unintended collisions with obstacles. This paper argues that Jeeves’s success depended crucially on the existence of the model. It also argues that models are generally useful in mobile robotics—even in tasks as simple as the one faced in this competition.
Active mobile robot localization by entropy minimization

Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot's effecters during localization. This paper proposes an active localization approach. The approach provides rational criteria for (1) setting the robot's motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximate world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.
Explanation Based Learning for Mobile Robot Perception to Appear In: Symbolic Visual Learning

Although machine learning techniques have been applied with remarkable success to several problems of computer perception and vision, most of these problems have been fairly simple in nature. The di culty with scaling up to more complex tasks is that inductive learning methods require a very large number of training examples in order to generalize correctly from complex sensor data. This chapter proposes an approach to overcoming this di culty, by relying on previously learned information to augment the available training data. In particular, we consider the task faced by a mobile robot learning to recognize new objects within an already-familiar environment. Because the robot has previously operated within this environment (here the corridors of a particular building), it has already had the opportunity to learn certain regularities that can be useful in subsequent learning tasks. Given a new task, such as learning to recognize the distance to the next door in the corridor, knowledge of these regularities enables the system to learn the new task more accurately from a limited quantity of new training data. We describe the Explanation-Based Neural Network (EBNN) algorithm for utilizing previously learned knowledge, and examine its performance for the mobile robot perception task of door recognition in a familiar corridor based on color vision and sonar sensor data. Experimental results indicate that EBNN is able to generalize more accurately than purely inductive methods such as Backpropagation, even when its prior knowledge is only approximately correct.
Active Mobile Robot Localization

Localization is the problem of determining the position of a mobile robot from sensor data. Most existing localization approaches are passive, i.e., they do not exploit the opportunity to control the robot’s effectors during localization. This paper proposes an active localization approach. The approach provides rational criteria for (1) setting the robot’s motion direction (exploration), and (2) determining the pointing direction of the sensors so as to most efficiently localize the robot. Furthermore, it is able to deal with noisy sensors and approximative world models. The appropriateness of our approach is demonstrated empirically using a mobile robot in a structured office environment.
Discovering Structure in Multiple Learning Tasks: The TC Algorithm

Recently, there has been an increased interest in “lifelong” machine learning methods, that transfer knowledge across multiple learning tasks. Such methods have repeatedly been found to outperform conventional, single-task learning algorithms when the learning tasks are appropriately related. To increase robustness of such approaches, methods are desirable that can reason about the relatedness of individual learning tasks, in order to avoid the danger arising from tasks that are unrelated and thus potentially misleading. This paper describes the task-clustering (TC) algorithm. TC clusters learning tasks into classes of mutually related tasks. When facing a new learning task, TC first determines the most related task cluster, then exploits information selectively from this task cluster only. An empirical study carried out in a mobile robot domain shows that TC outperforms its non-selective counterpart in situations where only a small number of tasks is relevant.
Learning Maps for Indoor Mobile Robot Navigation.

Autonomous robots must be able to learn and maintain models of their environments. Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid- based maps, by partitioning the latter into coherent regions. By combining both paradigms-grid-based and topological-, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room environments.
The Role of Transfer in Learning (extended abstract)

Virtually all of today’s approaches to artificial neural network learning generalize considerably well if sufficiently many training examples are available. However, they often work poorly when training data is scarce. Various psychological studies have illustrated that humans are able to generalize accurately even when training data is extremely scarce. Often, we generalize correctly from just a single training instance. In order to do so, we appear to massively re-use knowledge acquired in our previous lifetime. Lifelong learning is a framework that addresses the issue of knowledge re-use and inductive transfer in learning. In lifelong learning, it is assumed that the learner faces an entire family of learning tasks, not just a single one. When facing a new learning task, the learner may transferknowledge acquired in previous learning tasks to boost generalization. Three questions are of fundamental importance for any approach to lifelong learning: The whatthat is being transfered, the how it is being transferred, and the whenit is that it is being transferred.
Recent Advances in Robot Learning

Machine Learning.- Real- Learning, to Plan for Robust  Robot Programming by Demonstration (RPD): Supporting the Induction by Human Interaction.- Performance Improvement of Robot Continuous-Path Operation through Iterative Learning Using Neural Networks.- Learning Controllers for Industrial Robots.- Active Learning for Vision-Based Robot Grasping.- Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning.- Learning Concepts from Sensor Data of a Mobile Robot.
Explanation-based neural network learning

This chapter introduces the major learning approach studied in this book: the explanation-based neural network learning algorithm (EBNN). EBNN approaches the meta-level learning problem by learning a theory of the domain. This domain theory is domain-specific. It characterizes, for example, the relevance of individual features, their cross-dependencies, or certain invariant properties of the domain that apply to all learning tasks within the domain. Obviously, when the learner has a model of such regularities, there is an opportunity to generalize more accurately or, alternatively, learn from less training data. This is because without knowledge about these regularities the learner has to learn them from scratch, which necessarily requires more training data. EBNN transfers previously learned knowledge by explaining and analyzing training examples in terms of the domain theory. The result of this analysis is a set of domain-specific shape constraints for the function to be learned at the base-level. Thus, these constraints guide the base-level learning of new functions in a knowledgeable, domain-specific way.
The Invariance Approach

This chapter considers a particular family of lifelong learning problems. The lifelong supervised learning framework applies the idea of lifelong learning in a concrete (and restrictive) context: the learner is assumed to face supervised learning problems of the same type and, moreover, these learning problems must be related by some domain-specific properties (casted as invariances) that are unknown in the beginning of lifelong learning but can be learned. Central to the learning approach taken here is the domain theory. It consists of a single network, called the invariance network, which represents invariances that exist for all target functions. EBNN analyzes training examples using the invariance network, in order to guide generalization when learning a new function. As will be illustrated, knowing the invariances of the domain can be most instrumental for successful learning if training data is scarce.
Controlling synchro-drive robots with the dynamic window approach to collision avoidance

This paper proposes the dynamic window approach to reactive collision avoidance for mobile robots equipped with synchro-drives. The approach is derived directly from the motion dynamics of the robot and is therefore particularly well-suited for robots operating at high speed. It differs from previous approaches in that the search for commands controlling the translational and rotational velocity of the robot is carried out directly in the space of velocities. The advantage of our approach is that it correctly and in a rigorous way incorporates the dynamics of the robot. This is done by reducing the search space to the dynamic window, which consists of the velocities reachable within a short time interval. Within the dynamic window the approach only considers admissible velocities yielding a trajectory on which the robot is able to stop safely. Among these velocities the combination of translational and rotational velocity is chosen by maximizing an objective function. The objective function includes a measure of progress towards a goal location, the forward velocity of the robot, and the distance to the next obstacle on the trajectory. In extensive experiments the approach presented here has been found to safely control our mobile robot RHINO with speeds of up to 95 cm/sec, in populated and dynamic environments.
Learning Topological Maps: An Alternative Approach

Our goal is autonomous real-time control of a mobile robot. In this paper we want to show a possibility to learn topological maps of a large-scale indoor environment autonomously. In the literature there are two paradigms how to store information on the environment of a robot: as a grid-based (geometric) or as a topological map. While grid-based maps are considerably easy to learn and maintain, topological maps are quite compact and facilitate fast motionplanning.
Integrating Grid-Based and Topological Maps for Mobile Robot Navigation

Research on mobile robot navigation has produced two major paradigms for mapping indoor environments: grid-based and topological. While grid-based methods produce accurate metric maps, their complexity often prohibits efficient planning and problem solving in large-scale indoor environments. Topological maps, on the other hand, can be used much more efficiently, yet accurate and consistent topological maps are considerably difficult to learn in large-scale environments. 
 
This paper describes an approach that integrates both paradigms: grid-based and topological. Grid-based maps are learned using artificial neural networks and Bayesian integration. Topological maps are generated on top of the grid-based maps, by partitioning the latter into coherent regions. By combining both paradigms--grid-based and topological--, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency. The paper gives results for autonomously operating a mobile robot equipped with sonar sensors in populated multi-room environments.
LEARNING MORE FROM LESS DATA: EXPERIMENTS WITH LIFELONG ROBOT LEARNING

Learning more accurate functions from less data is a key issue in robot learning. This paper investigates robot learning in a lifelong learning framework. In lifelong learning, the learner faces an entire collection of learning tasks, not just a single one. Thus, it provides the opportunity for synergy among multiple tasks. To obtain this synergy, the central question in lifelong learning is how can the learner transfer knowledge across multiple tasks. In this paper we describe a selective approach to lifelong learning, the task clustering (TC) algorithm. TC transfers knowledge across multiple tasks by adjusting the distance metric in nearest neighbour generalization. To increase robustness to unrelated tasks, TC arranges all learning tasks hierarchically. When a new learning task arrives, TC relates it to the task hierarchy, in order to transfers knowledge selectively from the most related tasks only. As a result, TC is more robust than its unselective counterpart. Thus far, TC has been successfully applied to perception tasks involving visual and ultrasonic input, using our mobile robot XAVIER.