770 research outputs found
Deep Divergence-Based Approach to Clustering
A promising direction in deep learning research consists in learning
representations and simultaneously discovering cluster structure in unlabeled
data by optimizing a discriminative loss function. As opposed to supervised
deep learning, this line of research is in its infancy, and how to design and
optimize suitable loss functions to train deep neural networks for clustering
is still an open question. Our contribution to this emerging field is a new
deep clustering network that leverages the discriminative power of
information-theoretic divergence measures, which have been shown to be
effective in traditional clustering. We propose a novel loss function that
incorporates geometric regularization constraints, thus avoiding degenerate
structures of the resulting clustering partition. Experiments on synthetic
benchmarks and real datasets show that the proposed network achieves
competitive performance with respect to other state-of-the-art methods, scales
well to large datasets, and does not require pre-training steps
Information-Theoretic Active Perception for Multi-Robot Teams
Multi-robot teams that intelligently gather information have the potential to transform industries as diverse as agriculture, space exploration, mining, environmental monitoring, search and rescue, and construction. Despite large amounts of research effort on active perception problems, there still remain significant challenges. In this thesis, we present a variety of information-theoretic control policies that enable teams of robots to efficiently estimate different quantities of interest. Although these policies are intractable in general, we develop a series of approximations that make them suitable for real time use.
We begin by presenting a unified estimation and control scheme based on Shannon\u27s mutual information that lets small teams of robots equipped with range-only sensors track a single static target. By creating approximate representations, we substantially reduce the complexity of this approach, letting the team track a mobile target. We then scale this approach to larger teams that need to localize a large and unknown number of targets.
We also examine information-theoretic control policies to autonomously construct 3D maps with ground and aerial robots. By using Cauchy-Schwarz quadratic mutual information, we show substantial computational improvements over similar information-theoretic measures. To map environments faster, we adopt a hierarchical planning approach which incorporates trajectory optimization so that robots can quickly determine feasible and locally optimal trajectories. Finally, we present a high-level planning algorithm that enables heterogeneous robots to cooperatively construct maps
Collaborative Human-Robot Exploration via Implicit Coordination
This paper develops a methodology for collaborative human-robot exploration
that leverages implicit coordination. Most autonomous single- and multi-robot
exploration systems require a remote operator to provide explicit guidance to
the robotic team. Few works consider how to embed the human partner alongside
robots to provide guidance in the field. A remaining challenge for
collaborative human-robot exploration is efficient communication of goals from
the human to the robot. In this paper we develop a methodology that implicitly
communicates a region of interest from a helmet-mounted depth camera on the
human's head to the robot and an information gain-based exploration objective
that biases motion planning within the viewpoint provided by the human. The
result is an aerial system that safely accesses regions of interest that may
not be immediately viewable or reachable by the human. The approach is
evaluated in simulation and with hardware experiments in a motion capture
arena. Videos of the simulation and hardware experiments are available at:
https://youtu.be/7jgkBpVFIoE.Comment: 7 pages, 10 figures, to appear in the 2022 IEEE International
Symposium on Safety, Security, and Rescue Robotics (SSRR
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