6^th World Convention on Robots, Autonomous Vehicles and Deep Learning September 10-11, 2018 Singapore

Human–robot interaction is the study of interactions between humans and robots. It is often referred as HRI by researchers. Human–robot interaction is a multidisciplinary field with contributions from human–computer interaction, artificial intelligence, robotics, natural language understanding, design, and social sciences. Human has been a topic of both science fiction and academic speculation even before any robots existed. Because HRI depends on knowledge of (sometimes natural) human communication, many aspects of HRI are continuations of human communications topics that are much older than robotics.

Robots play a vital role in exploring the hostile environment of outer space. Besides the Earth, the Moon is the only celestial body that humans have stepped on. However, advancements in research are making it possible for robotic missions to reach space faster and gather more information than humans. Robots don’t get tired, can operate in airless environment and do not get bored or distracted, making them superior to humans in a lot of ways. Recently, Underwater Robotics has grown from nascent navigation and control algorithms for underwater and surface vehicles, to powered autonomous underwater vehicles routinely able to dive Pacific beyond 6000meters. We have seen underwater gliders cross the Atlantic Ocean and unmanned surface platforms (Wave Gliders) cross the Underwater Robotics as a field is set up to make a major contribution to understanding large scale societal problems. Emerging marine robotic developments will afford scientists advanced tools to explore and exploit the oceans at an unprecedented scale, in a sustainable manner. Robots play a vital role in exploring the hostile environment of outer space. Besides the Earth, the Moon is the only celestial body that humans have stepped on. However, advancements in research are making it possible for robotic missions to reach space faster and gather more information than humans. Robots don’t get tired, can operate in airless environment and do not get bored or distracted, making them superior to humans in a lot of ways.

An autonomous robot is a robot that performs behaviours or tasks with a high degree of autonomy, which is particularly desirable in fields such as spaceflight, household maintenance (such as cleaning), waste water treatment and delivering goods and services. Some modern factory robots are "autonomous" within the strict confines of their direct environment. It may not be that every degree of freedom exists in their surrounding environment, but the factory robot's workplace is challenging and can often contain chaotic, unpredicted variables. The exact orientation and position of the next object of work and (in the more advanced factories) even the type of object and the required task must be determined. One important area of robotics research is to enable the robot to cope with its environment whether this is on land, underwater, in the air, underground, or in space.

Sophisticated technology, for a majority of manufacturing activities in fabrication, forming, machining and assembly facilities, will be a significant contributor to productivity improvement with substantial gains in the quality of products in the face of tough challenge and competition Industrial Robots have been in use for about 50 years. The Present-Day Robots at Work: Industrial Robots have come to play a widespread and crucial role in many industrial operations today. These robots are almost always of the Jacquard type—with few human features— rather than the Jacquet-Droz, doll-like style. The work that robots do can be classified into three major categories: in the assembly and finishing of products; in the movement of materials and objects; and in the performance of work in environmentally difficult or hazardous situations.

Robotic surgery or robot-assisted surgery, allows doctors to perform many types of complex procedures with more precision, flexibility and control than is possible with conventional techniques. Robotic surgery is usually associated with minimally invasive surgery procedures performed through tiny incisions. It is also sometimes used in certain traditional open surgical procedures. Robots are currently used not just for prostate surgery, but for hysterectomies, the removal of fibroids, joint replacements, open-heart surgery and kidney surgeries. They can be used along with MRIs to provide organ biopsies. Since the physician can see images of the patient and control the robot through a computer, he/she does not need to be in the room, or even at the same location as the patient. This means that hospitals must evaluate the cost of the machine vs. the cost of traditional care. If robotic surgery cuts down on the trauma and healing time, there is money saved in terms of the number of days the patient stays in the hospital. There is also a reduction in the amount of personnel needed in the operating room during surgery.

A humanoid robot is a robot with its body shape built to resemble the human body. A humanoid design might be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as the study of bipedal locomotion, or for other purposes. In general, humanoid robots have a torso, a head, two arms, and two legs; though some forms of humanoid robots may model only part of the body, for example, from the waist up. Some humanoid robots also have heads designed to replicate human facial features such as eyes and mouths. Androids are humanoid robots built to aesthetically resemble humans

Flying opens new opportunities to robotically perform services and tasks like search and rescue, observation, mapping or even inspection and maintenance. As such, substantial interest in aerial robots has grown in recent years. Key areas to be addressed include, but are not limited to, innovative Unmanned Aerial Vehicles design, autonomous missions, guidance, navigation and control, airworthiness, safety and certification, risk assessment, multi-vehicle coordination, UAS traffic management (UTM).

Computational intelligence is the study of the design of intelligent agents. An agent is something that acts in an environment—it does something. Agents include worms, dogs, thermostats, airplanes, humans, organizations, and society. An intelligent agent is a system that acts intelligently: What it does is appropriate for its circumstances and its goal, it is flexible to changing environments and changing goals, it learns from experience, and it makes appropriate choices given perceptual limitations and finite computation. The central scientific goal of computational intelligence is to understand the principles that make intelligent behavior possible, in natural or artificial systems. The main hypothesis is that reasoning is computation.

Deep learning is the science of training large artificial neural networks. Deep neural networks (DNNs) can have hundreds of millions of parameters allowing them to model complex functions such as nonlinear dynamics. They form compact representations of state from raw, high-dimensional, multimodal sensor data commonly found in robotic systems and unlike many machine learning methods, they do not require a human expert to hand-engineer feature vectors from sensor data at design time. DNNs can, however, present particular challenges in physical robotic systems, where generating training data is generally expensive, and sub-optimal performance in training poses a danger in some applications. Yet, despite such challenges, robotcists are finding creative alternatives, such as leveraging training data via digital manipulation, automating training, and employing multiple DNNs to improve performance and reduce training time.

As a scientific discipline, computer vision is concerned with the theory and technology for building artificial systems that obtain information from images or multi-dimensional data. A significant part of artificial intelligence deals with planning or deliberation for system which can perform mechanical actions such as moving a robot through some environment. This type of processing typically needs input data provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot. Other parts which sometimes are described as belonging to artificial intelligence and which are used in relation to computer vision is pattern recognition and learning techniques. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding.

According to recent survey of an emerging, cutting-edge, and multi-disciplinary field of research at the intersection of Robotics and Wireless Sensor Networks (WSN) which refer to as Robotic Wireless Sensor Networks (RWSN). Survey defines a Robotic Wireless Sensor Network as an autonomous networked multi-robot system that aims to achieve certain sensing goals while meeting and maintaining certain communication performance requirements, through cooperative control, learning and adaptation. While both of the component areas, i.e., Robotics and WSN, are very well-known and well-explored, there exist a whole set of new opportunities and research directions at the intersection of these two fields which are relatively or even completely unexplored. One such example would be the use of a set of robotic routers to set up a temporary communication path between a sender and a receiver that uses the controlled mobility to the advantage of packet routing. 

Natural Language Processing (NLP) refers to AI method of communicating with intelligent systems using a natural language such as English. Processing of Natural Language is required when you want an intelligent system like robot to perform as per your instructions, when you want to hear decision from a dialogue based clinical expert system, etc. The field of NLP involves making computers to perform useful tasks with the natural languages humans use Nat­ur­al Lan­guage Pro­cessing is a field that cov­ers com­puter un­der­stand­ing and ma­nip­u­la­tion of hu­man lan­guage, and it’s ripe with pos­sib­il­it­ies for news­gath­er­ing, The study of natural language processing has been around for more than 50 years and grew out of the field of linguistics with the rise of computers.

In artificial intelligence, an intelligent agent (IA) is an autonomous entity which observes through sensors and acts upon an environment using actuators (i.e. it is an agent) and directs its activity towards achieving goals. Intelligent agents may also learn or use knowledge to achieve their goals. They may be very simple or very complex: a reflex machine such as a thermostat is considered an example of an intelligent agent. Intelligent agents are often described schematically as an abstract functional system similar to a computer program. For this reason, intelligent agents are sometimes called abstract intelligent agents to distinguish them from their real world implementations as computer systems, biological systems, or organizations. Some definitions of intelligent agents emphasize their autonomy, and so prefer the term autonomous intelligent agents.

An intelligent transportation system (ITS) is an advanced application which, without embodying intelligence as such, aims to provide innovative services relating to different modes of transport and traffic management and enable various users to be better informed and make safer, more coordinated, and 'smarter' use of transport networks. An intelligent Transportation system is an emerging transportation system which is comprised of an advanced information and telecommunications network for users, roads and vehicles. An intelligent Transportation system is the integrated application of advanced Technologies using electronics, computers, communications, and advanced sensors. These applications provide travellers with important information while improving the safety and efficiency of the transportation system

Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning. Pattern recognition systems are in many cases trained from labelled "training" data, but when no labelled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning) Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform "most likely" matching of the inputs, taking into account their statistical variation. This is opposed to pattern matching algorithms, which look for exact matches in the input with pre-existing patterns In contrast to pattern recognition; pattern matching is generally not considered a type of machine learning, although pattern-matching algorithms can sometimes succeed in providing similar-quality output of the sort provided by pattern-recognition algorithms.

The Internet of things (IoT) is the network of physical devices, vehicles, home appliances, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to connect and exchange data. Each thing is uniquely identifiable through its embedded computing system but is able to inter-operate within the existing Internet infrastructure The Internet of things allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention. When Internet of things is augmented with sensors and actuators, the technology becomes an instance of the more general class of cyber-physical systems, which also encompasses technologies such as smart grids, virtual power plants, smart homes, intelligent transportation and smart cities