980 research outputs found
Advanced Mobile Robotics: Volume 3
Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective
Computer-aided position planning of miniplates to treat facial bone defects
In this contribution, a software system for computer-aided position planning
of miniplates to treat facial bone defects is proposed. The intra-operatively
used bone plates have to be passively adapted on the underlying bone contours
for adequate bone fragment stabilization. However, this procedure can lead to
frequent intra-operatively performed material readjustments especially in
complex surgical cases. Our approach is able to fit a selection of common
implant models on the surgeon's desired position in a 3D computer model. This
happens with respect to the surrounding anatomical structures, always including
the possibility of adjusting both the direction and the position of the used
osteosynthesis material. By using the proposed software, surgeons are able to
pre-plan the out coming implant in its form and morphology with the aid of a
computer-visualized model within a few minutes. Further, the resulting model
can be stored in STL file format, the commonly used format for 3D printing.
Using this technology, surgeons are able to print the virtual generated
implant, or create an individually designed bending tool. This method leads to
adapted osteosynthesis materials according to the surrounding anatomy and
requires further a minimum amount of money and time.Comment: 19 pages, 13 Figures, 2 Table
Translating Videos to Commands for Robotic Manipulation with Deep Recurrent Neural Networks
We present a new method to translate videos to commands for robotic
manipulation using Deep Recurrent Neural Networks (RNN). Our framework first
extracts deep features from the input video frames with a deep Convolutional
Neural Networks (CNN). Two RNN layers with an encoder-decoder architecture are
then used to encode the visual features and sequentially generate the output
words as the command. We demonstrate that the translation accuracy can be
improved by allowing a smooth transaction between two RNN layers and using the
state-of-the-art feature extractor. The experimental results on our new
challenging dataset show that our approach outperforms recent methods by a fair
margin. Furthermore, we combine the proposed translation module with the vision
and planning system to let a robot perform various manipulation tasks. Finally,
we demonstrate the effectiveness of our framework on a full-size humanoid robot
WALK-MAN
Head-mounted Sensory Augmentation Device: Comparing Haptic and Audio Modality
This paper investigates and compares the effectiveness of haptic and audio modality for navigation in low visibility environment using a sensory augmentation device. A second generation head-mounted vibrotactile interface as a sensory augmentation prototype was developed to help users to navigate in such environments. In our experiment, a subject navigates along a wall relying on the haptic or audio feedbacks as navigation commands. Haptic/audio feedback is presented to the subjects according to the information measured from the walls to a set of 12 ultrasound sensors placed around a helmet and a classification algorithm by using multilayer perceptron neural network. Results showed the haptic modality leads to significantly lower route deviation in navigation compared to auditory feedback. Furthermore, the NASA TLX questionnaire showed that subjects reported lower cognitive workload with haptic modality although both modalities were able to navigate the users along the wall
A Developmental Organization for Robot Behavior
This paper focuses on exploring how learning and development can be structured in synthetic (robot) systems. We present a developmental assembler for constructing reusable and temporally extended actions in a sequence. The discussion adopts the traditions
of dynamic pattern theory in which behavior
is an artifact of coupled dynamical systems
with a number of controllable degrees of freedom. In our model, the events that delineate
control decisions are derived from the pattern
of (dis)equilibria on a working subset of sensorimotor policies. We show how this architecture can be used to accomplish sequential
knowledge gathering and representation tasks
and provide examples of the kind of developmental milestones that this approach has
already produced in our lab
Read and Reap the Rewards: Learning to Play Atari with the Help of Instruction Manuals
High sample complexity has long been a challenge for RL. On the other hand,
humans learn to perform tasks not only from interaction or demonstrations, but
also by reading unstructured text documents, e.g., instruction manuals.
Instruction manuals and wiki pages are among the most abundant data that could
inform agents of valuable features and policies or task-specific environmental
dynamics and reward structures. Therefore, we hypothesize that the ability to
utilize human-written instruction manuals to assist learning policies for
specific tasks should lead to a more efficient and better-performing agent. We
propose the Read and Reward framework. Read and Reward speeds up RL algorithms
on Atari games by reading manuals released by the Atari game developers. Our
framework consists of a QA Extraction module that extracts and summarizes
relevant information from the manual and a Reasoning module that evaluates
object-agent interactions based on information from the manual. An auxiliary
reward is then provided to a standard A2C RL agent, when interaction is
detected. Experimentally, various RL algorithms obtain significant improvement
in performance and training speed when assisted by our design
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