1,270 research outputs found
Comparing Computing Platforms for Deep Learning on a Humanoid Robot
The goal of this study is to test two different computing platforms with
respect to their suitability for running deep networks as part of a humanoid
robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH
and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU
processing. The experiments addressed a number of benchmarking tasks including
pedestrian detection using deep neural networks. Some of the results were
unexpected but demonstrate that platforms exhibit both advantages and
disadvantages when taking computational performance and electrical power
requirements of such a system into account.Comment: 12 pages, 5 figure
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
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