1,506 research outputs found
How hard is it to cross the room? -- Training (Recurrent) Neural Networks to steer a UAV
This work explores the feasibility of steering a drone with a (recurrent)
neural network, based on input from a forward looking camera, in the context of
a high-level navigation task. We set up a generic framework for training a
network to perform navigation tasks based on imitation learning. It can be
applied to both aerial and land vehicles. As a proof of concept we apply it to
a UAV (Unmanned Aerial Vehicle) in a simulated environment, learning to cross a
room containing a number of obstacles. So far only feedforward neural networks
(FNNs) have been used to train UAV control. To cope with more complex tasks, we
propose the use of recurrent neural networks (RNN) instead and successfully
train an LSTM (Long-Short Term Memory) network for controlling UAVs. Vision
based control is a sequential prediction problem, known for its highly
correlated input data. The correlation makes training a network hard,
especially an RNN. To overcome this issue, we investigate an alternative
sampling method during training, namely window-wise truncated backpropagation
through time (WW-TBPTT). Further, end-to-end training requires a lot of data
which often is not available. Therefore, we compare the performance of
retraining only the Fully Connected (FC) and LSTM control layers with networks
which are trained end-to-end. Performing the relatively simple task of crossing
a room already reveals important guidelines and good practices for training
neural control networks. Different visualizations help to explain the behavior
learned.Comment: 12 pages, 30 figure
Neural Network Memory Architectures for Autonomous Robot Navigation
This paper highlights the significance of including memory structures in
neural networks when the latter are used to learn perception-action loops for
autonomous robot navigation. Traditional navigation approaches rely on global
maps of the environment to overcome cul-de-sacs and plan feasible motions. Yet,
maintaining an accurate global map may be challenging in real-world settings. A
possible way to mitigate this limitation is to use learning techniques that
forgo hand-engineered map representations and infer appropriate control
responses directly from sensed information. An important but unexplored aspect
of such approaches is the effect of memory on their performance. This work is a
first thorough study of memory structures for deep-neural-network-based robot
navigation, and offers novel tools to train such networks from supervision and
quantify their ability to generalize to unseen scenarios. We analyze the
separation and generalization abilities of feedforward, long short-term memory,
and differentiable neural computer networks. We introduce a new method to
evaluate the generalization ability by estimating the VC-dimension of networks
with a final linear readout layer. We validate that the VC estimates are good
predictors of actual test performance. The reported method can be applied to
deep learning problems beyond robotics
Evaluation of Manufactured Product Performance Using Neural Networks
This paper discusses some of the several successful applications of neural networks which have made them a useful simulation tool. After several years of neglect, confidence in the accuracy of neural networks began to grow from the 1980s with applications in power, control and instrumentation and robotics to mention a few. Several successful industrial implementations of neural networks in the field of electrical engineering will be reviewed and results of the authors’ research in the areas of food security and health will also be presented. The research results will show that successful neural simulation results using Neurosolutions software also translated to successful realtime implementation of cost-effective products with reliable overall performance of up to 90%
Hand Gesture Recognition for Sign Language Transcription
Sign Language is a language which allows mute people to communicate with other mute or non-mute people. The benefits provided by this language, however, disappear when one of the members of a group does not know Sign Language and a conversation starts using that language. In this document, I present a system that takes advantage of Convolutional Neural Networks to recognize hand letter and number gestures from American Sign Language based on depth images captured by the Kinect camera. In addition, as a byproduct of these research efforts, I collected a new dataset of depth images of American Sign Language letters and numbers, and I compared the presented method for image recognition against a similar dataset but for Vietnamese Sign Language. Finally, I present how this work supports my ideas for the future work on a complete system for Sign Language transcription
- …