1,124 research outputs found
A Portable Active Binocular Robot Vision Architecture for Scene Exploration
We present a portable active binocular robot vision archi-
tecture that integrates a number of visual behaviours. This vision archi-
tecture inherits the abilities of vergence, localisation, recognition and si-
multaneous identification of multiple target object instances. To demon-
strate the portability of our vision architecture, we carry out qualitative
and comparative analysis under two different hardware robotic settings,
feature extraction techniques and viewpoints. Our portable active binoc-
ular robot vision architecture achieved average recognition rates of 93.5%
for fronto-parallel viewpoints and, 83% percentage for anthropomorphic
viewpoints, respectively
Towards binocular active vision in a robot head system
This paper presents the first results of an investigation and pilot study into an active, binocular vision system that combines binocular vergence, object recognition and attention control in a unified framework. The prototype developed is capable of identifying, targeting, verging on and recognizing objects in a highly-cluttered scene without the need for calibration or other knowledge of the camera geometry. This is achieved by implementing all image analysis in a symbolic space without creating explicit pixel-space maps. The system structure is based on the âsearchlight metaphorâ of biological systems. We present results of a first pilot investigation that yield a maximum vergence error of 6.4 pixels, while seven of nine known objects were recognized in a high-cluttered environment. Finally a âstepping stoneâ visual search strategy was demonstrated, taking a total of 40 saccades to find two known objects in the workspace, neither of which appeared simultaneously within the Field of View resulting from any individual saccade
Glasgow's Stereo Image Database of Garments
To provide insight into cloth perception and manipulation with an active
binocular robotic vision system, we compiled a database of 80 stereo-pair
colour images with corresponding horizontal and vertical disparity maps and
mask annotations, for 3D garment point cloud rendering has been created and
released. The stereo-image garment database is part of research conducted under
the EU-FP7 Clothes Perception and Manipulation (CloPeMa) project and belongs to
a wider database collection released through CloPeMa (www.clopema.eu). This
database is based on 16 different off-the-shelve garments. Each garment has
been imaged in five different pose configurations on the project's binocular
robot head. A full copy of the database is made available for scientific
research only at https://sites.google.com/site/ugstereodatabase/.Comment: 7 pages, 6 figure, image databas
Single-Shot Clothing Category Recognition in Free-Configurations with Application to Autonomous Clothes Sorting
This paper proposes a single-shot approach for recognising clothing
categories from 2.5D features. We propose two visual features, BSP (B-Spline
Patch) and TSD (Topology Spatial Distances) for this task. The local BSP
features are encoded by LLC (Locality-constrained Linear Coding) and fused with
three different global features. Our visual feature is robust to deformable
shapes and our approach is able to recognise the category of unknown clothing
in unconstrained and random configurations. We integrated the category
recognition pipeline with a stereo vision system, clothing instance detection,
and dual-arm manipulators to achieve an autonomous sorting system. To verify
the performance of our proposed method, we build a high-resolution RGBD
clothing dataset of 50 clothing items of 5 categories sampled in random
configurations (a total of 2,100 clothing samples). Experimental results show
that our approach is able to reach 83.2\% accuracy while classifying clothing
items which were previously unseen during training. This advances beyond the
previous state-of-the-art by 36.2\%. Finally, we evaluate the proposed approach
in an autonomous robot sorting system, in which the robot recognises a clothing
item from an unconstrained pile, grasps it, and sorts it into a box according
to its category. Our proposed sorting system achieves reasonable sorting
success rates with single-shot perception.Comment: 9 pages, accepted by IROS201
A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot
Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors
A hierarchical system for a distributed representation of the peripersonal space of a humanoid robot
Reaching a target object in an unknown and unstructured environment is easily performed by human beings. However, designing a humanoid robot that executes the same task requires the implementation of complex abilities, such as identifying the target in the visual field, estimating its spatial location, and precisely driving the motors of the arm to reach it. While research usually tackles the development of such abilities singularly, in this work we integrate a number of computational models into a unified framework, and demonstrate in a humanoid torso the feasibility of an integrated working representation of its peripersonal space. To achieve this goal, we propose a cognitive architecture that connects several models inspired by neural circuits of the visual, frontal and posterior parietal cortices of the brain. The outcome of the integration process is a system that allows the robot to create its internal model and its representation of the surrounding space by interacting with the environment directly, through a mutual adaptation of perception and action. The robot is eventually capable of executing a set of tasks, such as recognizing, gazing and reaching target objects, which can work separately or cooperate for supporting more structured and effective behaviors
Intrinsically Motivated Learning of Visual Motion Perception and Smooth Pursuit
We extend the framework of efficient coding, which has been used to model the
development of sensory processing in isolation, to model the development of the
perception/action cycle. Our extension combines sparse coding and reinforcement
learning so that sensory processing and behavior co-develop to optimize a
shared intrinsic motivational signal: the fidelity of the neural encoding of
the sensory input under resource constraints. Applying this framework to a
model system consisting of an active eye behaving in a time varying
environment, we find that this generic principle leads to the simultaneous
development of both smooth pursuit behavior and model neurons whose properties
are similar to those of primary visual cortical neurons selective for different
directions of visual motion. We suggest that this general principle may form
the basis for a unified and integrated explanation of many perception/action
loops.Comment: 6 pages, 5 figure
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