25 research outputs found
Middleware platform for distributed applications incorporating robots, sensors and the cloud
Cyber-physical systems in the factory of the future
will consist of cloud-hosted software governing an agile
production process executed by autonomous mobile robots
and controlled by analyzing the data from a vast number of
sensors. CPSs thus operate on a distributed production floor
infrastructure and the set-up continuously changes with each
new manufacturing task. In this paper, we present our OSGibased
middleware that abstracts the deployment of servicebased
CPS software components on the underlying distributed
platform comprising robots, actuators, sensors and the cloud.
Moreover, our middleware provides specific support to develop
components based on artificial neural networks, a technique that
recently became very popular for sensor data analytics and robot
actuation. We demonstrate a system where a robot takes actions
based on the input from sensors in its vicinity
Lazy Evaluation of Convolutional Filters
In this paper we propose a technique which avoids the evaluation of certain
convolutional filters in a deep neural network. This allows to trade-off the
accuracy of a deep neural network with the computational and memory
requirements. This is especially important on a constrained device unable to
hold all the weights of the network in memory
Learning to Grasp from a single demonstration
Learning-based approaches for robotic grasping using visual sensors typically
require collecting a large size dataset, either manually labeled or by many
trial and errors of a robotic manipulator in the real or simulated world. We
propose a simpler learning-from-demonstration approach that is able to detect
the object to grasp from merely a single demonstration using a convolutional
neural network we call GraspNet. In order to increase robustness and decrease
the training time even further, we leverage data from previous demonstrations
to quickly fine-tune a GrapNet for each new demonstration. We present some
preliminary results on a grasping experiment with the Franka Panda cobot for
which we can train a GraspNet with only hundreds of train iterations.Comment: 10 pages, 5 figures, IAS-15 2018 workshop on Learning Applications
for Intelligent Autonomous Robot