1,377 research outputs found
Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics
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Increasing the Efficiency of 6-DoF Visual Localization Using Multi-Modal Sensory Data
Localization is a key requirement for mobile robot autonomy and human-robot
interaction. Vision-based localization is accurate and flexible, however, it
incurs a high computational burden which limits its application on many
resource-constrained platforms. In this paper, we address the problem of
performing real-time localization in large-scale 3D point cloud maps of
ever-growing size. While most systems using multi-modal information reduce
localization time by employing side-channel information in a coarse manner (eg.
WiFi for a rough prior position estimate), we propose to inter-weave the map
with rich sensory data. This multi-modal approach achieves two key goals
simultaneously. First, it enables us to harness additional sensory data to
localise against a map covering a vast area in real-time; and secondly, it also
allows us to roughly localise devices which are not equipped with a camera. The
key to our approach is a localization policy based on a sequential Monte Carlo
estimator. The localiser uses this policy to attempt point-matching only in
nodes where it is likely to succeed, significantly increasing the efficiency of
the localization process. The proposed multi-modal localization system is
evaluated extensively in a large museum building. The results show that our
multi-modal approach not only increases the localization accuracy but
significantly reduces computational time.Comment: Presented at IEEE-RAS International Conference on Humanoid Robots
(Humanoids) 201
Model-Based Environmental Visual Perception for Humanoid Robots
The visual perception of a robot should answer two fundamental questions: What? and Where? In order to properly and efficiently reply to these questions, it is essential to establish a bidirectional coupling between the external stimuli and the internal representations. This coupling links the physical world with the inner abstraction models by sensor transformation, recognition, matching and optimization algorithms. The objective of this PhD is to establish this sensor-model coupling
Object detection and recognition with event driven cameras
This thesis presents study, analysis and implementation of algorithms
to perform object detection and recognition using an event-based cam
era. This sensor represents a novel paradigm which opens a wide range
of possibilities for future developments of computer vision. In partic
ular it allows to produce a fast, compressed, illumination invariant
output, which can be exploited for robotic tasks, where fast dynamics
and signi\ufb01cant illumination changes are frequent. The experiments
are carried out on the neuromorphic version of the iCub humanoid
platform. The robot is equipped with a novel dual camera setup
mounted directly in the robot\u2019s eyes, used to generate data with a
moving camera. The motion causes the presence of background clut
ter in the event stream.
In such scenario the detection problem has been addressed with an at
tention mechanism, speci\ufb01cally designed to respond to the presence of
objects, while discarding clutter. The proposed implementation takes
advantage of the nature of the data to simplify the original proto
object saliency model which inspired this work.
Successively, the recognition task was \ufb01rst tackled with a feasibility
study to demonstrate that the event stream carries su\ufb03cient informa
tion to classify objects and then with the implementation of a spiking
neural network. The feasibility study provides the proof-of-concept
that events are informative enough in the context of object classi\ufb01
cation, whereas the spiking implementation improves the results by
employing an architecture speci\ufb01cally designed to process event data.
The spiking network was trained with a three-factor local learning rule
which overcomes weight transport, update locking and non-locality
problem.
The presented results prove that both detection and classi\ufb01cation can
be carried-out in the target application using the event data
DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self
This paper introduces a cognitive architecture for a humanoid robot to engage in a proactive, mixed-initiative exploration and manipulation of its environment, where the initiative can originate from both the human and the robot. The framework, based on a biologically-grounded theory of the brain and mind, integrates a reactive interaction engine, a number of state-of-the art perceptual and motor learning algorithms, as well as planning abilities and an autobiographical memory. The architecture as a whole drives the robot behavior to solve the symbol grounding problem, acquire language capabilities, execute goal-oriented behavior, and express a verbal narrative of its own experience in the world. We validate our approach in human-robot interaction experiments with the iCub humanoid robot, showing that the proposed cognitive architecture can be applied in real time within a realistic scenario and that it can be used with naive users
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