Journal: Robotics and Autonomous Systems

Abbreviation

Robot. auton. syst.

Publisher

Elsevier

Journal Volumes

ISSN

0921-8890

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Publications 1 - 10 of 35
  • A decomposition approach to multi-vehicle cooperative control
    Item type: Journal Article
    Earl, Matthew G.; D'Andrea, Raffaello (2007)
    Robotics and Autonomous Systems
  • Bayesian space conceptualization and place classification for semantic maps in mobile robotics
    Item type: Journal Article
    Vasudevan, Shrihari; Siegwart, Roland (2008)
    Robotics and Autonomous Systems
  • Multisensor On-the-Fly Localization
    Item type: Journal Article
    Arras, Kai O.; Tomatis, Nicola; Jensen, Björn T.; et al. (2001)
    Robotics and Autonomous Systems
  • Robox at Expo.02
    Item type: Journal Article
    Siegwart, Roland; Arras, Kai O.; Bouabdallah, Samir; et al. (2003)
    Robotics and Autonomous Systems
  • Robox at Expo.02: A large-scale installation of personal robots
    Item type: Conference Paper
    Siegwart, Roland; Arras, Kai O.; Bouabdallah, Samir; et al. (2003)
    Robotics and Autonomous Systems
  • Compact Q-Learning Optimized for Micro-robots with Processing and Memory Constraints
    Item type: Conference Paper
    Asadpour, Masoud; Siegwart, Roland (2004)
    Robotics and Autonomous Systems
    Scaling down robots to miniature size introduces many new challenges including memory and program size limitations, low processor performance and low power autonomy. In this paper we describe the concept and implementation of learning of a safe-wandering task with the autonomous micro-robots, Alice. We propose a simplified reinforcement learning algorithm based on one-step Q-learning that is optimized in speed and memory consumption. This algorithm uses only integer-based sum operators and avoids floating-point and multiplication operators. Finally, quality of learning is compared to a floating-point based algorithm.
  • Automatic extension of a symbolic mobile manipulation skill set
    Item type: Journal Article
    Förster, Julian; Ott, Lionel; Nieto, Juan; et al. (2023)
    Robotics and Autonomous Systems
    Symbolic planning can provide an intuitive interface for non-expert users to operate autonomous robots by abstracting away much of the low-level programming. However, symbolic planners assume that the initially provided abstract domain and problem descriptions are closed and complete. This means that they are fundamentally unable to adapt to changes in the environment or tasks that are not captured by the initial description. We propose a method that allows an agent to automatically extend the abstract description of its skill set upon encountering such a situation. We introduce strategies for generalizing from previous experience, completing sequences of key actions and discovering preconditions to ensure computational efficiency. The resulting system is evaluated on a symbolic planning benchmark task and on object rearrangement tasks in simulation. Compared to a Monte Carlo Tree Search baseline, our strategies for efficient search have on average a 25% higher success rate at a 67% faster runtime. Code is available at https://github.com/ethz-asl/high_level_planning.
  • New technologies for testing a model of cricket phonotaxis on an outdoor robot
    Item type: Journal Article
    Reeve, Richard; Webb, Barbara; Horchler, Andrew; et al. (2005)
    Robotics and Autonomous Systems
  • Innovative design for wheeled locomotion in rough terrain
    Item type: Journal Article
    Siegwart, Roland; Lamon, Pierre; Estier, Thomas; et al. (2002)
    Robotics and Autonomous Systems
  • Robot learning from demonstration
    Item type: Other Journal Item
    Billard, Aude; Siegwart, Roland (2004)
    Robotics and Autonomous Systems
Publications 1 - 10 of 35