439 research outputs found
Color Classification and Object Recognition for Robot Soccer Under Variable Illumination
Griffith Sciences, School of Information and Communication TechnologyFull Tex
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar
TRAIL Team Description Paper for RoboCup@Home 2023
Our team, TRAIL, consists of AI/ML laboratory members from The University of
Tokyo. We leverage our extensive research experience in state-of-the-art
machine learning to build general-purpose in-home service robots. We previously
participated in two competitions using Human Support Robot (HSR): RoboCup@Home
Japan Open 2020 (DSPL) and World Robot Summit 2020, equivalent to RoboCup World
Tournament. Throughout the competitions, we showed that a data-driven approach
is effective for performing in-home tasks. Aiming for further development of
building a versatile and fast-adaptable system, in RoboCup @Home 2023, we unify
three technologies that have recently been evaluated as components in the
fields of deep learning and robot learning into a real household robot system.
In addition, to stimulate research all over the RoboCup@Home community, we
build a platform that manages data collected from each site belonging to the
community around the world, taking advantage of the characteristics of the
community
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