1,159 research outputs found
Multi-criteria Evolution of Neural Network Topologies: Balancing Experience and Performance in Autonomous Systems
Majority of Artificial Neural Network (ANN) implementations in autonomous
systems use a fixed/user-prescribed network topology, leading to sub-optimal
performance and low portability. The existing neuro-evolution of augmenting
topology or NEAT paradigm offers a powerful alternative by allowing the network
topology and the connection weights to be simultaneously optimized through an
evolutionary process. However, most NEAT implementations allow the
consideration of only a single objective. There also persists the question of
how to tractably introduce topological diversification that mitigates
overfitting to training scenarios. To address these gaps, this paper develops a
multi-objective neuro-evolution algorithm. While adopting the basic elements of
NEAT, important modifications are made to the selection, speciation, and
mutation processes. With the backdrop of small-robot path-planning
applications, an experience-gain criterion is derived to encapsulate the amount
of diverse local environment encountered by the system. This criterion
facilitates the evolution of genes that support exploration, thereby seeking to
generalize from a smaller set of mission scenarios than possible with
performance maximization alone. The effectiveness of the single-objective
(optimizing performance) and the multi-objective (optimizing performance and
experience-gain) neuro-evolution approaches are evaluated on two different
small-robot cases, with ANNs obtained by the multi-objective optimization
observed to provide superior performance in unseen scenarios
Socially Cognizant Robotics for a Technology Enhanced Society
Emerging applications of robotics, and concerns about their impact, require
the research community to put human-centric objectives front-and-center. To
meet this challenge, we advocate an interdisciplinary approach, socially
cognizant robotics, which synthesizes technical and social science methods. We
argue that this approach follows from the need to empower stakeholder
participation (from synchronous human feedback to asynchronous societal
assessment) in shaping AI-driven robot behavior at all levels, and leads to a
range of novel research perspectives and problems both for improving robots'
interactions with individuals and impacts on society. Drawing on these
arguments, we develop best practices for socially cognizant robot design that
balance traditional technology-based metrics (e.g. efficiency, precision and
accuracy) with critically important, albeit challenging to measure, human and
society-based metrics
SACSoN: Scalable Autonomous Data Collection for Social Navigation
Machine learning provides a powerful tool for building socially compliant
robotic systems that go beyond simple predictive models of human behavior. By
observing and understanding human interactions from past experiences, learning
can enable effective social navigation behaviors directly from data. However,
collecting navigation data in human-occupied environments may require
teleoperation or continuous monitoring, making the process prohibitively
expensive to scale. In this paper, we present a scalable data collection system
for vision-based navigation, SACSoN, that can autonomously navigate around
pedestrians in challenging real-world environments while encouraging rich
interactions. SACSoN uses visual observations to observe and react to humans in
its vicinity. It couples this visual understanding with continual learning and
an autonomous collision recovery system that limits the involvement of a human
operator, allowing for better dataset scaling. We use a this system to collect
the SACSoN dataset, the largest-of-its-kind visual navigation dataset of
autonomous robots operating in human-occupied spaces, spanning over 75 hours
and 4000 rich interactions with humans. Our experiments show that collecting
data with a novel objective that encourages interactions, leads to significant
improvements in downstream tasks such as inferring pedestrian dynamics and
learning socially compliant navigation behaviors. We make videos of our
autonomous data collection system and the SACSoN dataset publicly available on
our project page.Comment: 9 pages, 12 figures, 4 table
Tracking Target Signal Strengths on a Grid using Sparsity
Multi-target tracking is mainly challenged by the nonlinearity present in the
measurement equation, and the difficulty in fast and accurate data association.
To overcome these challenges, the present paper introduces a grid-based model
in which the state captures target signal strengths on a known spatial grid
(TSSG). This model leads to \emph{linear} state and measurement equations,
which bypass data association and can afford state estimation via
sparsity-aware Kalman filtering (KF). Leveraging the grid-induced sparsity of
the novel model, two types of sparsity-cognizant TSSG-KF trackers are
developed: one effects sparsity through -norm regularization, and the
other invokes sparsity as an extra measurement. Iterative extended KF and
Gauss-Newton algorithms are developed for reduced-complexity tracking, along
with accurate error covariance updates for assessing performance of the
resultant sparsity-aware state estimators. Based on TSSG state estimates, more
informative target position and track estimates can be obtained in a follow-up
step, ensuring that track association and position estimation errors do not
propagate back into TSSG state estimates. The novel TSSG trackers do not
require knowing the number of targets or their signal strengths, and exhibit
considerably lower complexity than the benchmark hidden Markov model filter,
especially for a large number of targets. Numerical simulations demonstrate
that sparsity-cognizant trackers enjoy improved root mean-square error
performance at reduced complexity when compared to their sparsity-agnostic
counterparts.Comment: Submitted to IEEE Trans. on Signal Processin
A COMPARATIVE ANALYSIS OF GLOBAL POSITIONING SYSTEM SCHEMES BASED ON BLOCK CODES
Global Positioning System (GPS) is a satellite based positioning system based on radio ranging technique. The GPS will provide very accurate three-dimensional position, velocity and timing information to users anywhere in the world. GPS can also be used in other applications such as vehicle monitoring for traffic management in urban areas, Geographical Information System (GIS), 4G Communications, marine navigation, search and rescue and military applications. As GPS accuracy is limited by ionospheric effects, this course also covers the basics of ionosphere and its effects on GPS. Navigation is the art of directing a vehicle such as aircraft or a person from one point to another point. Some of the prominent advantages of the GPS are: Land based system problems like ground reflections, electromagnetic interference, reflections from physical systems are avoided in GPS since it is space constellation, Intentional interference like jamming, unintentional interference will not affect GPS since spread spectrum techniques are used in it, System accuracy can be improved to the order of centimeters using differential techniques, Smaller size and reduced cost of the GPS receiver enable it to be used in 3G Communication. In this paper, a literature review of existing GPS schemes based on block codes that mainly targets towards finding out the tolerance to signals from other GPS satellites sharing the same frequency band (multiple access capability), analyzing the tolerance to some level of multipath interference, there are many potential sources of multipath reflection (example man-made or natural object) and finding out the tolerance to reasonable levels of unintentional or intentional interference, jamming or spoofing by signal designed to mimic a GPS signal
Multi-task Deep Reinforcement Learning with PopArt
The reinforcement learning community has made great strides in designing
algorithms capable of exceeding human performance on specific tasks. These
algorithms are mostly trained one task at the time, each new task requiring to
train a brand new agent instance. This means the learning algorithm is general,
but each solution is not; each agent can only solve the one task it was trained
on. In this work, we study the problem of learning to master not one but
multiple sequential-decision tasks at once. A general issue in multi-task
learning is that a balance must be found between the needs of multiple tasks
competing for the limited resources of a single learning system. Many learning
algorithms can get distracted by certain tasks in the set of tasks to solve.
Such tasks appear more salient to the learning process, for instance because of
the density or magnitude of the in-task rewards. This causes the algorithm to
focus on those salient tasks at the expense of generality. We propose to
automatically adapt the contribution of each task to the agent's updates, so
that all tasks have a similar impact on the learning dynamics. This resulted in
state of the art performance on learning to play all games in a set of 57
diverse Atari games. Excitingly, our method learned a single trained policy -
with a single set of weights - that exceeds median human performance. To our
knowledge, this was the first time a single agent surpassed human-level
performance on this multi-task domain. The same approach also demonstrated
state of the art performance on a set of 30 tasks in the 3D reinforcement
learning platform DeepMind Lab
Reactive Reinforcement Learning in Asynchronous Environments
The relationship between a reinforcement learning (RL) agent and an
asynchronous environment is often ignored. Frequently used models of the
interaction between an agent and its environment, such as Markov Decision
Processes (MDP) or Semi-Markov Decision Processes (SMDP), do not capture the
fact that, in an asynchronous environment, the state of the environment may
change during computation performed by the agent. In an asynchronous
environment, minimizing reaction time---the time it takes for an agent to react
to an observation---also minimizes the time in which the state of the
environment may change following observation. In many environments, the
reaction time of an agent directly impacts task performance by permitting the
environment to transition into either an undesirable terminal state or a state
where performing the chosen action is inappropriate. We propose a class of
reactive reinforcement learning algorithms that address this problem of
asynchronous environments by immediately acting after observing new state
information. We compare a reactive SARSA learning algorithm with the
conventional SARSA learning algorithm on two asynchronous robotic tasks
(emergency stopping and impact prevention), and show that the reactive RL
algorithm reduces the reaction time of the agent by approximately the duration
of the algorithm's learning update. This new class of reactive algorithms may
facilitate safer control and faster decision making without any change to
standard learning guarantees.Comment: 11 pages, 7 figures, currently under journal peer revie
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