15,389 research outputs found
On the simulation of interactive non-verbal behaviour in virtual humans
Development of virtual humans has focused mainly in two broad areas - conversational agents and computer game characters. Computer game characters have traditionally been action-oriented - focused on the game-play - and conversational agents have been focused on sensible/intelligent conversation. While virtual humans have incorporated some form of non-verbal behaviour, this has been quite limited and more importantly not connected or connected very loosely with the behaviour of a real human interacting with the virtual human - due to a lack of sensor data and no system to respond to that data. The interactional aspect of non-verbal behaviour is highly important in human-human interactions and previous research has demonstrated that people treat media (and therefore virtual humans) as real people, and so interactive non-verbal behaviour is also important in the development of virtual humans. This paper presents the challenges in creating virtual humans that are non-verbally interactive and drawing corollaries with the development history of control systems in robotics presents some approaches to solving these challenges - specifically using behaviour based systems - and shows how an order of magnitude increase in response time of virtual humans in conversation can be obtained and that the development of rapidly responding non-verbal behaviours can start with just a few behaviours with more behaviours added without difficulty later in development
Visualizing the Structure of Large Trees
This study introduces a new method of visualizing complex tree structured
objects. The usefulness of this method is illustrated in the context of
detecting unexpected features in a data set of very large trees. The major
contribution is a novel two-dimensional graphical representation of each tree,
with a covariate coded by color. The motivating data set contains three
dimensional representations of brain artery systems of 105 subjects. Due to
inaccuracies inherent in the medical imaging techniques, issues with the
reconstruction algo- rithms and inconsistencies introduced by manual
adjustment, various discrepancies are present in the data. The proposed
representation enables quick visual detection of the most common discrepancies.
For our driving example, this tool led to the modification of 10% of the artery
trees and deletion of 6.7%. The benefits of our cleaning method are
demonstrated through a statistical hypothesis test on the effects of aging on
vessel structure. The data cleaning resulted in improved significance levels.Comment: 17 pages, 8 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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