An Ontology-Based Expert System for the Systematic Design of Humanoid Robots

Die Entwicklung humanoider Roboter ist eine zeitaufwendige, komplexe und herausfordernde Aufgabe. Daher stellt diese Thesis einen neuen, systematischen Ansatz vor, der es erlaubt, Expertenwissen zum Entwurf humanoider Roboter zu konservieren, um damit zukünftige Entwicklungen zu unterstützen. Der Ansatz kann in drei aufeinanderfolgende Schritte unterteilt werden. Im ersten Schritt wird Wissen zum Entwurf humanoider Roboter durch die Entwicklung von Roboterkomponenten und die Analyse verwandter Arbeiten gewonnen. Dieses Wissen wird im zweiten Schritt formalisiert und in Form einer ontologischen Wissensbasis gespeichert. Im letzten Schritt wird diese Wissensbasis von einem Expertensystem verwendet, um Lösungsvorschläge zum Entwurf von Roboterkomponenten auf Grundlage von Benutzeranforderungen zu generieren. Der Ansatz wird anhand von Fallstudien zu Komponenten des humanoiden Roboters ARMAR-6 evaluiert: Sensor-Aktor-Controller-Einheiten für Robotergelenke und Roboterhände

Distributed Dynamic Hierarchical Task Assignment for Human-Robot Teams

This work implements a joint task architecture for human-robot collaborative task execution using a hierarchical task planner. This architecture allowed humans and robots to work together as teammates in the same environment while following several task constraints. These constraints are 1) sequential order, 2) non-sequential, and 3) alternative execution constraints. Both the robot and the human are aware of each other's current state and allocate their next task based on the task tree. On-table tasks, such as setting up a tea table or playing a color sequence matching game, validate the task architecture. The robot will have an updated task representation of its human teammate's task. Using this knowledge, it is also able to continuously detect the human teammate's intention towards each sub-task and coordinate it with the teammate. While performing a joint task, there can be situations in which tasks overlap or do not overlap. We designed a dialogue-based conversation between humans and robots to resolve conflict in the case of overlapping tasks.Evaluating the human-robot task architecture is the next concern after validating the task architecture. Trust and trustworthiness are some of the most critical metrics to explore. A study was conducted between humans and robots to create a homophily situation. Homophily means when a person feels biased towards another person because of having similarities in social ways. We conducted this study to determine whether humans can form a homophilic relationship with robots and whether there is a connection between homophily and trust. We found a correlation between homophily and trust in human-robot interactions.Furthermore, we designed a pipeline by which the robot learns a task by observing the human teammate's hand movement while conversing. The robot then constructs the tree by itself using a GA learning framework. Thus removing the need for manual specification by a programmer each time to revise or update the task tree which makes the architecture more flexible, realistic, efficient, and dynamic. Additionally, our architecture allows the robot to comprehend the context of a situation by conversing with a human teammate and observing the surroundings. The robot can find a link between the context of the situation and the surrounding objects by using the ontology approach and can perform the desired task accordingly. Therefore, we proposed a human-robot distributed joint task management architecture that addresses design, improvement, and evaluation under multiple constraints

Context-Independent Task Knowledge for Neurosymbolic Reasoning in Cognitive Robotics

One of the current main goals of artificial intelligence and robotics research is the creation of an artificial assistant which can have flexible, human like behavior, in order to accomplish everyday tasks. A lot of what is context-independent task knowledge to the human is what enables this flexibility at multiple levels of cognition. In this scope the author analyzes how to acquire, represent and disambiguate symbolic knowledge representing context-independent task knowledge, abstracted from multiple instances: this thesis elaborates the incurred problems, implementation constraints, current state-of-the-art practices and ultimately the solutions newly introduced in this scope. The author specifically discusses acquisition of context-independent task knowledge from large amounts of human-written texts and their reusability in the robotics domain; the acquisition of knowledge on human musculoskeletal dependencies constraining motion which allows a better higher level representation of observed trajectories; the means of verbalization of partial contextual and instruction knowledge, increasing interaction possibilities with the human as well as contextual adaptation. All the aforementioned points are supported by evaluation in heterogeneous setups, to bring a view on how to make optimal use of statistical & symbolic applications (i.e. neurosymbolic reasoning) in cognitive robotics. This work has been performed to enable context-adaptable artificial assistants, by bringing together knowledge on what is usually regarded as context-independent task knowledge

MULTI-MODAL TASK INSTRUCTIONS TO ROBOTS BY NAIVE USERS

This thesis presents a theoretical framework for the design of user-programmable robots. The objective of the work is to investigate multi-modal unconstrained natural instructions given to robots in order to design a learning robot. A corpus-centred approach is used to design an agent that can reason, learn and interact with a human in a natural unconstrained way. The corpus-centred design approach is formalised and developed in detail. It requires the developer to record a human during interaction and analyse the recordings to find instruction primitives. These are then implemented into a robot. The focus of this work has been on how to combine speech and gesture using rules extracted from the analysis of a corpus. A multi-modal integration algorithm is presented, that can use timing and semantics to group, match and unify gesture and language. The algorithm always achieves correct pairings on a corpus and initiates questions to the user in ambiguous cases or missing information. The domain of card games has been investigated, because of its variety of games which are rich in rules and contain sequences. A further focus of the work is on the translation of rule-based instructions. Most multi-modal interfaces to date have only considered sequential instructions. The combination of frame-based reasoning, a knowledge base organised as an ontology and a problem solver engine is used to store these rules. The understanding of rule instructions, which contain conditional and imaginary situations require an agent with complex reasoning capabilities. A test system of the agent implementation is also described. Tests to confirm the implementation by playing back the corpus are presented. Furthermore, deployment test results with the implemented agent and human subjects are presented and discussed. The tests showed that the rate of errors that are due to the sentences not being defined in the grammar does not decrease by an acceptable rate when new grammar is introduced. This was particularly the case for complex verbal rule instructions which have a large variety of being expressed

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man

The Talking Heads experiment: Origins of words and meanings

The Talking Heads Experiment, conducted in the years 1999-2001, was the first large-scale experiment in which open populations of situated embodied agents created for the first time ever a new shared vocabulary by playing language games about real world scenes in front of them. The agents could teleport to different physical sites in the world through the Internet. Sites, in Antwerp, Brussels, Paris, Tokyo, London, Cambridge and several other locations were linked into the network. Humans could interact with the robotic agents either on site or remotely through the Internet and thus influence the evolving ontologies and languages of the artificial agents. The present book describes in detail the motivation, the cognitive mechanisms used by the agents, the various installations of the Talking Heads, the experimental results that were obtained, and the interaction with humans. It also provides a perspective on what happened in the field after these initial groundbreaking experiments. The book is invaluable reading for anyone interested in the history of agent-based models of language evolution and the future of Artificial Intelligence

The Talking Heads experiment: Origins of words and meanings

The Talking Heads Experiment, conducted in the years 1999-2001, was the first large-scale experiment in which open populations of situated embodied agents created for the first time ever a new shared vocabulary by playing language games about real world scenes in front of them. The agents could teleport to different physical sites in the world through the Internet. Sites, in Antwerp, Brussels, Paris, Tokyo, London, Cambridge and several other locations were linked into the network. Humans could interact with the robotic agents either on site or remotely through the Internet and thus influence the evolving ontologies and languages of the artificial agents. The present book describes in detail the motivation, the cognitive mechanisms used by the agents, the various installations of the Talking Heads, the experimental results that were obtained, and the interaction with humans. It also provides a perspective on what happened in the field after these initial groundbreaking experiments. The book is invaluable reading for anyone interested in the history of agent-based models of language evolution and the future of Artificial Intelligence