57,975 research outputs found
A learning-based shared control architecture for interactive task execution
Shared control is a key technology for various
robotic applications in which a robotic system and a human
operator are meant to collaborate efficiently. In order to achieve
efficient task execution in shared control, it is essential to
predict the desired behavior for a given situation or context
to simplify the control task for the human operator. To do this
prediction, we use Learning from Demonstration (LfD), which is
a popular approach for transferring human skills to robots. We
encode the demonstrated behavior as trajectory distributions
and generalize the learned distributions to new situations. The
goal of this paper is to present a shared control framework
that uses learned expert distributions to gain more autonomy.
Our approach controls the balance between the controller’s
autonomy and the human preference based on the distributions
of the demonstrated trajectories. Moreover, the learned
distributions are autonomously refined from collaborative task
executions, resulting in a master-slave system with increasing
autonomy that requires less user input with an increasing
number of task executions. We experimentally validated that
our shared control approach enables efficient task executions.
Moreover, the conducted experiments demonstrated that the
developed system improves its performances through interactive
task executions with our shared control
Teaching Concurrent Software Design: A Case Study Using Android
In this article, we explore various parallel and distributed computing topics
from a user-centric software engineering perspective. Specifically, in the
context of mobile application development, we study the basic building blocks
of interactive applications in the form of events, timers, and asynchronous
activities, along with related software modeling, architecture, and design
topics.Comment: Submitted to CDER NSF/IEEE-TCPP Curriculum Initiative on Parallel and
Distributed Computing - Core Topics for Undergraduate
TensorLayer: A Versatile Library for Efficient Deep Learning Development
Deep learning has enabled major advances in the fields of computer vision,
natural language processing, and multimedia among many others. Developing a
deep learning system is arduous and complex, as it involves constructing neural
network architectures, managing training/trained models, tuning optimization
process, preprocessing and organizing data, etc. TensorLayer is a versatile
Python library that aims at helping researchers and engineers efficiently
develop deep learning systems. It offers rich abstractions for neural networks,
model and data management, and parallel workflow mechanism. While boosting
efficiency, TensorLayer maintains both performance and scalability. TensorLayer
was released in September 2016 on GitHub, and has helped people from academia
and industry develop real-world applications of deep learning.Comment: ACM Multimedia 201
- …