613 research outputs found
Fast traffic sign recognition using color segmentation and deep convolutional networks
The use of Computer Vision techniques for the automatic
recognition of road signs is fundamental for the development of intelli-
gent vehicles and advanced driver assistance systems. In this paper, we
describe a procedure based on color segmentation, Histogram of Ori-
ented Gradients (HOG), and Convolutional Neural Networks (CNN) for
detecting and classifying road signs. Detection is speeded up by a pre-
processing step to reduce the search space, while classication is carried
out by using a Deep Learning technique. A quantitative evaluation of the
proposed approach has been conducted on the well-known German Traf-
c Sign data set and on the novel Data set of Italian Trac Signs (DITS),
which is publicly available and contains challenging sequences captured
in adverse weather conditions and in an urban scenario at night-time.
Experimental results demonstrate the eectiveness of the proposed ap-
proach in terms of both classication accuracy and computational speed
Plane extraction for indoor place recognition
In this paper, we present an image based plane extraction
method well suited for real-time operations. Our approach exploits the
assumption that the surrounding scene is mainly composed by planes
disposed in known directions. Planes are detected from a single image
exploiting a voting scheme that takes into account the vanishing lines.
Then, candidate planes are validated and merged using a region grow-
ing based approach to detect in real-time planes inside an unknown in-
door environment. Using the related plane homographies is possible to
remove the perspective distortion, enabling standard place recognition
algorithms to work in an invariant point of view setup. Quantitative Ex-
periments performed with real world images show the effectiveness of our
approach compared with a very popular method
DOP: Deep Optimistic Planning with Approximate Value Function Evaluation
Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. multi-agent systems or hyper-redundant robots). To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) plan effective policies. Specifically, we exploit deep neural networks to learn Q-functions that are used to attack the curse of dimensionality during a Monte-Carlo tree search. Our algorithm, in fact, constructs upper confidence bounds on the learned value function to select actions optimistically. We implement and evaluate DOP on different scenarios: (1) a cooperative navigation problem, (2) a fetching task for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot (both in simulation and real). The obtained results show the effectiveness of DOP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
Q-CP: Learning Action Values for Cooperative Planning
Research on multi-robot systems has demonstrated promising results in manifold applications and domains. Still, efficiently learning an effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. hyper-redundant and groups of robot). To alleviate this problem, we present Q-CP a cooperative model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) generate effective policies. Specifically, we exploit Q-learning to attack the curse-of-dimensionality in the iterations of a Monte-Carlo Tree Search. We implement and evaluate Q-CP on different stochastic cooperative (general-sum) games: (1) a simple cooperative navigation problem among 3 robots, (2) a cooperation scenario between a pair of KUKA YouBots performing hand-overs, and (3) a coordination task between two mobile robots entering a door. The obtained results show the effectiveness of Q-CP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance
Language-based sensing descriptors for robot object grounding
In this work, we consider an autonomous robot that is required
to understand commands given by a human through natural language.
Specifically, we assume that this robot is provided with an internal
representation of the environment. However, such a representation is unknown
to the user. In this context, we address the problem of allowing a
human to understand the robot internal representation through dialog.
To this end, we introduce the concept of sensing descriptors. Such representations
are used by the robot to recognize unknown object properties
in the given commands and warn the user about them. Additionally, we
show how these properties can be learned over time by leveraging past
interactions in order to enhance the grounding capabilities of the robot
Teaching robots parametrized executable plans through spoken interaction
While operating in domestic environments, robots will necessarily
face difficulties not envisioned by their developers at programming
time. Moreover, the tasks to be performed by a robot will often
have to be specialized and/or adapted to the needs of specific users
and specific environments. Hence, learning how to operate by interacting
with the user seems a key enabling feature to support the
introduction of robots in everyday environments.
In this paper we contribute a novel approach for learning, through
the interaction with the user, task descriptions that are defined as a
combination of primitive actions. The proposed approach makes
a significant step forward by making task descriptions parametric
with respect to domain specific semantic categories. Moreover, by
mapping the task representation into a task representation language,
we are able to express complex execution paradigms and to revise
the learned tasks in a high-level fashion. The approach is evaluated
in multiple practical applications with a service robot
Effective Target Aware Visual Navigation for UAVs
In this paper we propose an effective vision-based navigation method that
allows a multirotor vehicle to simultaneously reach a desired goal pose in the
environment while constantly facing a target object or landmark. Standard
techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual
Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast
maneuvers) do not allow to constantly maintain the line of sight with a target
of interest. Instead, we compute the optimal trajectory by solving a non-linear
optimization problem that minimizes the target re-projection error while
meeting the UAV's dynamic constraints. The desired trajectory is then tracked
by means of a real-time Non-linear Model Predictive Controller (NMPC): this
implicitly allows the multirotor to satisfy both the required constraints. We
successfully evaluate the proposed approach in many real and simulated
experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR)
201
New Neutral Gauge Bosons and New Heavy Fermions in the Light of the New LEP Data
We derive limits on a class of new physics effects that are naturally present
in grand unified theories based on extended gauge groups, and in particular in
and models. We concentrate on ) the effects of the mixing of
new neutral gauge bosons with the standard ; ) the effects of a mixing
of the known fermions with new heavy states. We perform a global analysis
including all the LEP data on the decay widths and asymmetries collected
until 1993, the SLC measurement of the left--right asymmetry, the measurement
of the boson mass, various charged current constraints, and the low energy
neutral current experiments. We use a top mass value in the range announced by
CDF. We derive limits on the -- mixing, which are always \lsim 0.01
and are at the level of a few {\it per mille} if some specific model is
assumed. Model-dependent theoretical relations between the mixing and the mass
of the new gauge boson in most cases require TeV. Limits on
light--heavy fermion mixings are also largely improved with respect to previous
analyses, and are particularly relevant for a class of models that we discuss.Comment: 12 pages (including two tables), revised version, accepted for
publication in Phys. Lett. B. Includes a discussion of the m_t and alpha_s
dependence of the bounds on the Z' mass and the fermion mixing
Does terrain slope really dominate goal searching?
If you can locate a target by using one reliable source of information, why would you use an unreliable one? A similar question has been faced in a recent study on homing pigeons, in which, despite the presence of better predictors of the goal location, the slope of the floor in an arena dominated the searching process. This piece of evidence seems to contradict straightforward accounts of associative learning, according to which behavior should be controlled by the stimulus that best predicts the reward, and has fueled interest toward one question that, to date, has received scarce attention in the field of spatial cognition: how are vertical spaces represented? The purpose of this communication is to briefly review the studies on this issue, trying to determine whether slope is a special cue—driving behavior irrespective of other cues—or simply a very salient one
Does terrain slope really dominate goal searching?
If you can locate a target by using one reliable source of information, why would you use an unreliable one? A similar question has been faced in a recent study on homing pigeons, in which, despite the presence of better predictors of the goal location, the slope of the floor in an arena dominated the searching process. This piece of evidence seems to contradict straightforward accounts of associative learning, according to which behavior should be controlled by the stimulus that best predicts the reward, and has fueled interest toward one question that, to date, has received scarce attention in the field of spatial cognition: how are vertical spaces represented? The purpose of this communication is to briefly review the studies on this issue, trying to determine whether slope is a special cue—driving behavior irrespective of other cues—or simply a very salient one
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