652 research outputs found
Distributed Algorithms for Stochastic Source Seeking With Mobile Robot Networks
Autonomous robot networks are an effective tool for monitoring large-scale environmental fields. This paper proposes distributed control strategies for localizing the source of a noisy signal, which could represent a physical quantity of interest such as magnetic force, heat, radio signal, or chemical concentration. We develop algorithms specific to two scenarios: one in which the sensors have a precise model of the signal formation process and one in which a signal model is not available. In the model-free scenario, a team of sensors is used to follow a stochastic gradient of the signal field. Our approach is distributed, robust to deformations in the group geometry, does not necessitate global localization, and is guaranteed to lead the sensors to a neighborhood of a local maximum of the field. In the model-based scenario, the sensors follow a stochastic gradient of the mutual information (MI) between their expected measurements and the expected source location in a distributed manner. The performance is demonstrated in simulation using a robot sensor network to localize the source of a wireless radio signal
Learning a Structured Neural Network Policy for a Hopping Task
In this work we present a method for learning a reactive policy for a simple
dynamic locomotion task involving hard impact and switching contacts where we
assume the contact location and contact timing to be unknown. To learn such a
policy, we use optimal control to optimize a local controller for a fixed
environment and contacts. We learn the contact-rich dynamics for our
underactuated systems along these trajectories in a sample efficient manner. We
use the optimized policies to learn the reactive policy in form of a neural
network. Using a new neural network architecture, we are able to preserve more
information from the local policy and make its output interpretable in the
sense that its output in terms of desired trajectories, feedforward commands
and gains can be interpreted. Extensive simulations demonstrate the robustness
of the approach to changing environments, outperforming a model-free gradient
policy based methods on the same tasks in simulation. Finally, we show that the
learned policy can be robustly transferred on a real robot.Comment: IEEE Robotics and Automation Letters 201
One or Two Things We know about Concept Drift -- A Survey on Monitoring Evolving Environments
The world surrounding us is subject to constant change. These changes,
frequently described as concept drift, influence many industrial and technical
processes. As they can lead to malfunctions and other anomalous behavior, which
may be safety-critical in many scenarios, detecting and analyzing concept drift
is crucial. In this paper, we provide a literature review focusing on concept
drift in unsupervised data streams. While many surveys focus on supervised data
streams, so far, there is no work reviewing the unsupervised setting. However,
this setting is of particular relevance for monitoring and anomaly detection
which are directly applicable to many tasks and challenges in engineering. This
survey provides a taxonomy of existing work on drift detection. Besides, it
covers the current state of research on drift localization in a systematic way.
In addition to providing a systematic literature review, this work provides
precise mathematical definitions of the considered problems and contains
standardized experiments on parametric artificial datasets allowing for a
direct comparison of different strategies for detection and localization.
Thereby, the suitability of different schemes can be analyzed systematically
and guidelines for their usage in real-world scenarios can be provided.
Finally, there is a section on the emerging topic of explaining concept drift
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