Semi-Structured Decision Processes: A Conceptual Framework for Understanding Human-Automation Decision Systems

The purpose of this work is to improve understanding of existing and proposed decision systems, ideally to improve the design of future systems. A "decision system" is defined as a collection of information-processing components -- often involving humans and automation (e.g., computers) -- that interact towards a common set of objectives. Since a key issue in the design of decision systems is the division of work between humans and machines (a task known as "function allocation"), this report is primarily intended to help designers incorporate automation more appropriately within these systems. This report does not provide a design methodology, but introduces a way to qualitatively analyze potential designs early in the system design process. A novel analytical framework is presented, based on the concept of "semi-Structured" decision processes. It is believed that many decisions involve both well-defined "Structured" parts (e.g., formal procedures, traditional algorithms) and ill-defined "Unstructured" parts (e.g., intuition, judgement, neural networks) that interact in a known manner. While Structured processes are often desired because they fully prescribe how a future decision (during "operation") will be made, they are limited by what is explicitly understood prior to operation. A system designer who incorporates Unstructured processes into a decision system understands which parts are not understood sufficiently, and relinquishes control by deferring decision-making from design to operation. Among other things, this design choice tends to add flexibility and robustness. The value of the semi-Structured framework is that it forces people to consider system design concepts as operational decision processes in which both well-defined and ill-defined components are made explicit. This may provide more insight into decision systems, and improve understanding of the implications of design choices. The first part of this report defines the semi-Structured process and introduces a diagrammatic notation for decision process models. In the second part, the semi-Structured framework is used to understand and explain highly evolved decision system designs (these are assumed to be representative of "good" designs) whose components include feedback controllers, alerts, decision aids, and displays. Lastly, the semi-Structured framework is applied to a decision system design for a mobile robot.Charles Stark Draper Laboratory, Inc., under IR&D effort 101

Tag Recognition for Quadcopter Drone Movement

Unmanned Aerial Vehicle (UAV) drone such as Parrot AR.Drone 2.0 is a flying mobile robot which has been popularly researched for the application of search and rescue mission. In this project, Robot Operating System (ROS), a free open source platform for developing robot control software is used to develop a tag recognition program for drone movement. ROS is popular with mobile robotics application development because sensors data transmission for robot control system analysis will be very handy with the use of ROS nodes and packages once the installation and compilation is done correctly. It is expected that the drone can communicate with a laptop via ROS nodes for sensors data transmission which will be further analyzed and processed for the close-loop control system. The developed program consisting of several packages is aimed to demonstrate the recognition of different tags by the drone which will be transformed into a movement command with respect to the tag recognized; in other words, a visual-based navigation program is developed

Modeling, Evaluation, and Scale on Artificial Pedestrians: A Literature Review

Modeling pedestrian dynamics and their implementation in a computer are challenging and important issues in the knowledge areas of transportation and computer simulation. The aim of this article is to provide a bibliographic outlook so that the reader may have quick access to the most relevant works related to this problem. We have used three main axes to organize the article's contents: pedestrian models, validation techniques, and multiscale approaches. The backbone of this work is the classification of existing pedestrian models; we have organized the works in the literature under five categories, according to the techniques used for implementing the operational level in each pedestrian model. Then the main existing validation methods, oriented to evaluate the behavioral quality of the simulation systems, are reviewed. Furthermore, we review the key issues that arise when facing multiscale pedestrian modeling, where we first focus on the behavioral scale (combinations of micro and macro pedestrian models) and second on the scale size (from individuals to crowds). The article begins by introducing the main characteristics of walking dynamics and its analysis tools and concludes with a discussion about the contributions that different knowledge fields can make in the near future to this exciting area

The State of Lifelong Learning in Service Robots: Current Bottlenecks in Object Perception and Manipulation

Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming

TRAINING AN AGENT TO MOVE TOWARDS A TARGET INTERACTING WITH A COMPLEX ENVIRONMENT

Στη σημερινή εποχή, το πρόβλημα της αυτόνομης πλοήγησης στα σύγχρονα κινητά ρομπότ αποτελεί σημείο ενδιαφέροντος για την πλειοψηφία της έρευνας που γίνεται γύρω από τη ρομποτική. Αυτό το θέμα γίνεται ακόμα πιο απαιτητικό, καθώς οι απαιτήσεις στα δυναμικά περιβάλλοντα περιλαμβάνουν αυτονομία υψηλού επιπέδου και ευέλικτες δυνατότητες λήψης αποφάσεων για το ρομπότ, ώστε να επιτευχθεί αποφυγής συγκρούσεων. Το Deep learning κατάφερε να λύσει κάποια κοινά ζητήματα στη ρομποτική, όπως η λήψη αποφάσεων, η πλοήγηση και ο έλεγχος, όμως, με εποπτευόμενο τρόπο. Οι Reinforcement learning τεχνολογίες έχουν συνδυαστεί με το Deep learning, με αποτέλεσμα ένα νέο ερευνητικό θέμα γνωστό ως deep reinforcement learning (DRL). Με τη χρήση του DRL, η διαδικασία μπορεί να αυτοματοποιηθεί με τη μετάφραση δεδομένων αισθητήρων πολλών διαστάσεων σε εντολές κίνησης ρομπότ χωρίς τη χρήση κεντρικοποιημένων πληροφοριών, παρέχοντας έναν μη εποπτευόμενο τρόπο. Αυτό που χρειάζεται, για να ενθαρρυνθεί ο agent μάθησης και μέσω διαδικασίας δοκιμής και σφάλματος με το περιβάλλον, να βρει την καλύτερη δράση για κάθε κατάσταση, είναι μία βαθμωτή συνάρτηση ανταμοιβής. Στην εν λόγω διατριβή, δημιουργήθηκε ένα προσομοιωμένο περιβάλλον με ένα κινητό ρομπότ που αλληλεπιδρά με αυτό. Δύο αλγόριθμοι βασισμένοι στο DRL, οι Actor-Critic και PPO, χρησιμοποιήθηκαν για να εκπαιδεύσουν τον παράγοντα να κινείται με ασφάλεια στο περιβάλλον, αποφεύγοντας τα εμπόδια και στοχεύοντας στην επίτευξη ενός καθορισμένου στόχου. Τα αποτελέσματά τους παρουσιάζονται και συγκρίνονται.Nowadays, the problem of autonomous navigation in modern mobile robots is the point of interest for the majority of research in robotics. This topic becomes even more challenging as the requirements in dynamic environments include high-level autonomy and flexible decision-making capabilities for the robot, to achieve collision avoidance. Deep learning has succeeded in solving some common issues in robotics, such as decision making, navigation and control, in a supervised manner though. Reinforcement learning frameworks have been combined with deep learning, resulting in a new research topic known as deep reinforcement learning (DRL). With the use of DRL the procedure can become automated by mapping high-dimensional sensory data to robot motion commands without using ground-truth information, providing an unsupervised manner. It simply takes a scalar reward function to encourage the learning agent through trial-and-error interactions with the environment, with the goal of finding the best action for each state. In the project thesis in question, a simulated environment was created with a mobile robot interacting with it. Two DRL-based algorithms, Actor-Critic and PPO were used to train the agent to move safely in the environment, avoiding the obstacles and aiming to reach a specified goal. Their results are presented and compared

HyperBody: An Experimental VR Game Exploring the Cosmotechnics of Game Fandom through a Posthumanist Lens

Interdependencies among ACGN (Anime, Comics, Games, and Novels) communities in China, Hong Kong, and Taiwan are growing. However, game studies and fan studies remain distinct disciplines. This cross-disciplinary thesis bridges this gap by investigating "game-fandom" practices in VR production, defined as the fusion of game and fan studies within the ACGN context. Drawing from Yuk Hui's "cosmotechnics" and Karen Barad's posthumanist perspective, this research reconsiders the relationship between cosmology, morality, and technology (Hui 2017). It employs "intra-action" to emphasise the indivisible, dynamic relations among specified objects (Barad 2007). Cultural practices in C-pop idol groups, Chinese BL (Boys' Love) novels, science fiction, and modding communities are analysed, illuminating the ACGN fandom's cultural, technological, and affective dimensions. This work features the creation, description, and evaluation of an experimental VR game, "HyperBody", which integrates the written thesis by reflecting game-fandom's cosmotechnics and intra-actions. The thesis offers two significant contributions: "queer tuning", a theory illuminating new cultural, technological, and affective turns within fandom and computational art, and a "diffractive" approach, forming a methodological framework for posthuman performative contexts. This diffractive framework enables practical contributions such as creating and describing experimental VR productions using the sound engine. It also highlights a thorough evaluation approach reconciling quantitative and qualitative methods in VR production analysis, investigating affective experiences, and exploring how users engage creatively with queer VR gamespaces. These contributions foster interdisciplinary collaboration among VR, game design, architecture, and fandom studies, underscoring the inextricable link among ethics, ontology, and epistemology, culminating in a proposed ethico-onto-epistem-ological framework

Drone deep reinforcement learning: A review

Unmanned Aerial Vehicles (UAVs) are increasingly being used in many challenging and diversified applications. These applications belong to the civilian and the military fields. To name a few; infrastructure inspection, traffic patrolling, remote sensing, mapping, surveillance, rescuing humans and animals, environment monitoring, and Intelligence, Surveillance, Target Acquisition, and Reconnaissance (ISTAR) operations. However, the use of UAVs in these applications needs a substantial level of autonomy. In other words, UAVs should have the ability to accomplish planned missions in unexpected situations without requiring human intervention. To ensure this level of autonomy, many artificial intelligence algorithms were designed. These algorithms targeted the guidance, navigation, and control (GNC) of UAVs. In this paper, we described the state of the art of one subset of these algorithms: the deep reinforcement learning (DRL) techniques. We made a detailed description of them, and we deduced the current limitations in this area. We noted that most of these DRL methods were designed to ensure stable and smooth UAV navigation by training computer-simulated environments. We realized that further research efforts are needed to address the challenges that restrain their deployment in real-life scenarios