221 research outputs found
Learning Semantic-Agnostic and Spatial-Aware Representation for Generalizable Visual-Audio Navigation
Visual-audio navigation (VAN) is attracting more and more attention from the
robotic community due to its broad applications, \emph{e.g.}, household robots
and rescue robots. In this task, an embodied agent must search for and navigate
to the sound source with egocentric visual and audio observations. However, the
existing methods are limited in two aspects: 1) poor generalization to unheard
sound categories; 2) sample inefficient in training. Focusing on these two
problems, we propose a brain-inspired plug-and-play method to learn a
semantic-agnostic and spatial-aware representation for generalizable
visual-audio navigation. We meticulously design two auxiliary tasks for
respectively accelerating learning representations with the above-desired
characteristics. With these two auxiliary tasks, the agent learns a
spatially-correlated representation of visual and audio inputs that can be
applied to work on environments with novel sounds and maps. Experiment results
on realistic 3D scenes (Replica and Matterport3D) demonstrate that our method
achieves better generalization performance when zero-shot transferred to scenes
with unseen maps and unheard sound categories
Evaluation of thermal conductivity of the constituent layers in TRISO particles using Raman spectroscopy
Pose-Assisted Multi-Camera Collaboration for Active Object Tracking
Active Object Tracking (AOT) is crucial to many visionbased applications,
e.g., mobile robot, intelligent surveillance. However, there are a number of
challenges when deploying active tracking in complex scenarios, e.g., target is
frequently occluded by obstacles. In this paper, we extend the single-camera
AOT to a multi-camera setting, where cameras tracking a target in a
collaborative fashion. To achieve effective collaboration among cameras, we
propose a novel Pose-Assisted Multi-Camera Collaboration System, which enables
a camera to cooperate with the others by sharing camera poses for active object
tracking. In the system, each camera is equipped with two controllers and a
switcher: The vision-based controller tracks targets based on observed images.
The pose-based controller moves the camera in accordance to the poses of the
other cameras. At each step, the switcher decides which action to take from the
two controllers according to the visibility of the target. The experimental
results demonstrate that our system outperforms all the baselines and is
capable of generalizing to unseen environments. The code and demo videos are
available on our website
https://sites.google.com/view/pose-assistedcollaboration
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