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Showing papers by "Sebastian Thrun published in 1996"


Proceedings Article
04 Aug 1996
TL;DR: By combining both paradigms--grid-based and topological--, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency.
Abstract: 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.

400 citations


Proceedings Article
01 Jan 1996
TL;DR: The task-clustering algorithm TC clusters learning tasks into classes of mutually related tasks, and outperforms its non-selective counterpart in situations where only a small number of tasks is relevant.
Abstract: 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.

242 citations


01 Apr 1996
TL;DR: By combining both paradigms-grid-based and topological-, the approach presented here gains the best of both worlds: accuracy/consistency and efficiency.
Abstract: : 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.

199 citations


Proceedings ArticleDOI
04 Nov 1996
TL;DR: The dynamic window approach to reactive collision avoidance for mobile robots equipped with synchro-drives is proposed and has been found to safely control the mobile robot RHINO with speeds of up to 95 cm/sec, in populated and dynamic environments.
Abstract: 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.

66 citations


Book
01 Jan 1996
TL;DR: This chapter introduces the major learning approach studied in this book: the explanation-based neural network learning algorithm (EBNN), which approaches the meta-level learning problem by learning a theory of the domain that characterizes the relevance of individual features, their cross-dependencies, or certain invariant properties of the Domain.
Abstract: 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

35 citations


BookDOI
01 Jan 1996
TL;DR: This paper presents a meta-modelling framework that allows for the simulation of the dynamic response of a real-world robot to be modeled through a network of neural networks.
Abstract: Machine Learning- Real-World Robotics: Learning, to Plan for Robust Execution- 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

10 citations


Proceedings ArticleDOI
01 Jan 1996
TL;DR: A selective approach to lifelong learning is described, the task clustering (TC) algorithm, which transfers knowledge across multiple tasks by adjusting the distance metric in nearest neighbour generalization and is more robust than its unselective counterpart.
Abstract: 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. (3 pages)

8 citations


Proceedings Article
04 Aug 1996
TL;DR: This paper wants to show a possibility to learn topological maps of a large-scale indoor environment autonomously and to demonstrate autonomous real-time control of a mobile robot.
Abstract: 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.

1 citations