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Proceedings of IJCAI International Workshop on Neural-Symbolic Learning and Reasoning NeSy 2005
Design and analysis of Intelligent Navigational controller for Mobile Robot
Since last several years requirement graph for autonomous mobile robots according to its virtual application has always been an upward one. Smother and faster mobile robots navigation with multiple function are the necessity of the day. This research is based on navigation system as well as kinematics model analysis for autonomous mobile robot in known environments. To execute and attain introductory robotic behaviour inside environments(e.g. obstacle avoidance, wall or edge following and target seeking) robot uses method of perception, sensor integration and fusion. With the help of these sensors robot creates its collision free path and analyse an environmental map time to time. Mobile robot navigation in an unfamiliar environment can be successfully studied here using online sensor fusion and integration. Various AI algorithm are used to describe overall procedure of mobilerobot navigation and its path planning problem. To design suitable controller that create
collision free path are achieved by the combined study of kinematics analysis of motion as well as an artificial intelligent technique. In fuzzy logic approach, a set of linguistic fuzzy rules are generated for navigation of mobile robot. An expert controller has been developed for the navigation in various condition of environment using these fuzzy rules. Further, type-2 fuzzy is employed to simplify and clarify the developed control algorithm more accurately due to fuzzy logic limitations. In addition, recurrent neural network (RNN) methodology has been analysed for robot navigation. Which helps the model at the time of learning stage. The robustness of controller has been checked on Webots simulation platform. Simulation results and performance of the controller using Webots platform show that, the mobile robot is capable for avoiding obstacles and reaching the termination point in efficient manner
Drama, a connectionist model for robot learning: experiments on grounding communication through imitation in autonomous robots
The present dissertation addresses problems related to robot learning from demonstra¬
tion. It presents the building of a connectionist architecture, which provides the robot
with the necessary cognitive and behavioural mechanisms for learning a synthetic lan¬
guage taught by an external teacher agent. This thesis considers three main issues:
1) learning of spatio-temporal invariance in a dynamic noisy environment, 2) symbol
grounding of a robot's actions and perceptions, 3) development of a common symbolic
representation of the world by heterogeneous agents.We build our approach on the assumption that grounding of symbolic communication
creates constraints not only on the cognitive capabilities of the agent but also and especially on its behavioural capacities. Behavioural skills, such as imitation, which allow
the agent to co-ordinate its actionn to that of the teacher agent, are required aside to
general cognitive abilities of associativity, in order to constrain the agent's attention
to making relevant perceptions, onto which it grounds the teacher agent's symbolic
expression. In addition, the agent should be provided with the cognitive capacity for
extracting spatial and temporal invariance in the continuous flow of its perceptions.
Based on this requirement, we develop a connectionist architecture for learning time
series. The model is a Dynamical Recurrent Associative Memory Architecture, called
DRAMA. It is a fully connected recurrent neural network using Hebbian update rules.
Learning is dynamic and unsupervised. The performance of the architecture is analysed theoretically, through numerical simulations and through physical and simulated
robotic experiments. Training of the network is computationally fast and inexpensive,
which allows its implementation for real time computation and on-line learning in a
inexpensive hardware system. Robotic experiments are carried out with different learning tasks involving recognition of spatial and temporal invariance, namely landmark
recognition and prediction of perception-action sequence in maze travelling.The architecture is applied to experiments on robot learning by imitation. A learner
robot is taught by a teacher agent, a human instructor and another robot, a vocabulary
to describe its perceptions and actions. The experiments are based on an imitative
strategy, whereby the learner robot reproduces the teacher's actions. While imitating
the teacher's movements, the learner robot makes similar proprio and exteroceptions
to those of the teacher. The learner robot grounds the teacher's words onto the set of
common perceptions they share. We carry out experiments in simulated and physical
environments, using different robotic set-ups, increasing gradually the complexity of
the task. In a first set of experiments, we study transmission of a vocabulary to
designate actions and perception of a robot. Further, we carry out simulation studies,
in which we investigate transmission and use of the vocabulary among a group of
robotic agents. In a third set of experiments, we investigate learning sequences of the
robot's perceptions, while wandering in a physically constrained environment. Finally,
we present the implementation of DRAMA in Robota, a doll-like robot, which can
imitate the arms and head movements of a human instructor. Through this imitative
game, Robota is taught to perform and label dance patterns. Further, Robota is taught
a basic language, including a lexicon and syntactical rules for the combination of words
of the lexicon, to describe its actions and perception of touch onto its body
Formal Methods for Autonomous Systems
Formal methods refer to rigorous, mathematical approaches to system
development and have played a key role in establishing the correctness of
safety-critical systems. The main building blocks of formal methods are models
and specifications, which are analogous to behaviors and requirements in system
design and give us the means to verify and synthesize system behaviors with
formal guarantees.
This monograph provides a survey of the current state of the art on
applications of formal methods in the autonomous systems domain. We consider
correct-by-construction synthesis under various formulations, including closed
systems, reactive, and probabilistic settings. Beyond synthesizing systems in
known environments, we address the concept of uncertainty and bound the
behavior of systems that employ learning using formal methods. Further, we
examine the synthesis of systems with monitoring, a mitigation technique for
ensuring that once a system deviates from expected behavior, it knows a way of
returning to normalcy. We also show how to overcome some limitations of formal
methods themselves with learning. We conclude with future directions for formal
methods in reinforcement learning, uncertainty, privacy, explainability of
formal methods, and regulation and certification
Action-oriented Scene Understanding
In order to allow robots to act autonomously it is crucial that they do not only describe their environment accurately but also identify how to interact with their surroundings.
While we witnessed tremendous progress in descriptive computer vision, approaches that explicitly target action are scarcer.
This cumulative dissertation approaches the goal of interpreting visual scenes “in the wild” with respect to actions implied by the scene. We call this approach action-oriented scene understanding. It involves identifying and judging opportunities for interaction with constituents of the scene (e.g. objects and their parts) as well as understanding object functions and how interactions will impact the future. All of these aspects are addressed on three levels of abstraction: elements, perception and reasoning.
On the elementary level, we investigate semantic and functional grouping of objects by analyzing annotated natural image scenes. We compare object label-based and visual context definitions with respect to their suitability for generating meaningful object class representations. Our findings suggest that representations generated from visual context are on-par in terms of semantic quality with those generated from large quantities of text.
The perceptive level concerns action identification. We propose a system to identify possible interactions for robots and humans with the environment (affordances) on a pixel level using state-of-the-art machine learning methods. Pixel-wise part annotations of images are transformed into 12 affordance maps. Using these maps, a convolutional neural network is trained to densely predict affordance maps from unknown RGB images. In contrast to previous work, this approach operates exclusively on RGB images during both, training and testing, and yet achieves state-of-the-art performance.
At the reasoning level, we extend the question from asking what actions are possible to what actions are plausible. For this, we gathered a dataset of household images associated with human ratings of the likelihoods of eight different actions. Based on the judgement provided by the human raters, we train convolutional neural networks to generate plausibility scores from unseen images.
Furthermore, having considered only static scenes previously in this thesis, we propose a system that takes video input and predicts plausible future actions. Since this requires careful identification of relevant features in the video sequence, we analyze this particular aspect in detail using a synthetic dataset for several state-of-the-art video models. We identify feature learning as a major obstacle for anticipation in natural video data.
The presented projects analyze the role of action in scene understanding from various angles and in multiple settings while highlighting the advantages of assuming an action-oriented perspective.
We conclude that action-oriented scene understanding can augment classic computer vision in many real-life applications, in particular robotics
GPU Computing for Cognitive Robotics
This thesis presents the first investigation of the impact of GPU
computing on cognitive robotics by providing a series of novel experiments in
the area of action and language acquisition in humanoid robots and computer
vision. Cognitive robotics is concerned with endowing robots with high-level
cognitive capabilities to enable the achievement of complex goals in complex
environments. Reaching the ultimate goal of developing cognitive robots will
require tremendous amounts of computational power, which was until
recently provided mostly by standard CPU processors. CPU cores are
optimised for serial code execution at the expense of parallel execution, which
renders them relatively inefficient when it comes to high-performance
computing applications. The ever-increasing market demand for
high-performance, real-time 3D graphics has evolved the GPU into a highly
parallel, multithreaded, many-core processor extraordinary computational
power and very high memory bandwidth. These vast computational resources
of modern GPUs can now be used by the most of the cognitive robotics models
as they tend to be inherently parallel. Various interesting and insightful
cognitive models were developed and addressed important scientific questions
concerning action-language acquisition and computer vision. While they have
provided us with important scientific insights, their complexity and
application has not improved much over the last years. The experimental
tasks as well as the scale of these models are often minimised to avoid
excessive training times that grow exponentially with the number of neurons
and the training data. This impedes further progress and development of
complex neurocontrollers that would be able to take the cognitive robotics
research a step closer to reaching the ultimate goal of creating intelligent
machines. This thesis presents several cases where the application of the GPU
computing on cognitive robotics algorithms resulted in the development of
large-scale neurocontrollers of previously unseen complexity enabling the
conducting of the novel experiments described herein.European Commission Seventh Framework
Programm
Natural Language Processing: Emerging Neural Approaches and Applications
This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains
Data-Driven Representation Learning in Multimodal Feature Fusion
abstract: Modern machine learning systems leverage data and features from multiple modalities to gain more predictive power. In most scenarios, the modalities are vastly different and the acquired data are heterogeneous in nature. Consequently, building highly effective fusion algorithms is at the core to achieve improved model robustness and inferencing performance. This dissertation focuses on the representation learning approaches as the fusion strategy. Specifically, the objective is to learn the shared latent representation which jointly exploit the structural information encoded in all modalities, such that a straightforward learning model can be adopted to obtain the prediction.
We first consider sensor fusion, a typical multimodal fusion problem critical to building a pervasive computing platform. A systematic fusion technique is described to support both multiple sensors and descriptors for activity recognition. Targeted to learn the optimal combination of kernels, Multiple Kernel Learning (MKL) algorithms have been successfully applied to numerous fusion problems in computer vision etc. Utilizing the MKL formulation, next we describe an auto-context algorithm for learning image context via the fusion with low-level descriptors. Furthermore, a principled fusion algorithm using deep learning to optimize kernel machines is developed. By bridging deep architectures with kernel optimization, this approach leverages the benefits of both paradigms and is applied to a wide variety of fusion problems.
In many real-world applications, the modalities exhibit highly specific data structures, such as time sequences and graphs, and consequently, special design of the learning architecture is needed. In order to improve the temporal modeling for multivariate sequences, we developed two architectures centered around attention models. A novel clinical time series analysis model is proposed for several critical problems in healthcare. Another model coupled with triplet ranking loss as metric learning framework is described to better solve speaker diarization. Compared to state-of-the-art recurrent networks, these attention-based multivariate analysis tools achieve improved performance while having a lower computational complexity. Finally, in order to perform community detection on multilayer graphs, a fusion algorithm is described to derive node embedding from word embedding techniques and also exploit the complementary relational information contained in each layer of the graph.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201
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