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

Interactive Learning of Probabilistic Decision Making by Service Robots with Multiple Skill Domains

This thesis makes a contribution to autonomous service robots, centered around two aspects. The first is modeling decision making in the face of incomplete information on top of diverse basic skills of a service robot. Second, based on such a model, it is investigated, how to transfer complex decision-making knowledge into the system. Interactive learning, naturally from both demonstrations of human teachers and in interaction with objects, yields decision-making models applicable by the robot

Infrastructure Design, Signalling and Security in Railway

Railway transportation has become one of the main technological advances of our society. Since the first railway used to carry coal from a mine in Shropshire (England, 1600), a lot of efforts have been made to improve this transportation concept. One of its milestones was the invention and development of the steam locomotive, but commercial rail travels became practical two hundred years later. From these first attempts, railway infrastructures, signalling and security have evolved and become more complex than those performed in its earlier stages. This book will provide readers a comprehensive technical guide, covering these topics and presenting a brief overview of selected railway systems in the world. The objective of the book is to serve as a valuable reference for students, educators, scientists, faculty members, researchers, and engineers

Towards learning mechanistic models at the right level of abstraction

Das menschliche Gehirn ist in der Lage, Vorhersagen zu treffen, zu planen und sich durch mentale Simulationen kontrafaktische Situationen vorzustellen. Künstliche neuronale Netze sind zwar in bestimmten Bereichen brereits sehr leistungsfähig, scheinen aber immer noch ein mechanistisches Verständnis der Welt zu vermissen. In dieser Arbeit befassen wir uns mit verschiedenen Ansätzen, wie neuronale Netze die zugrundeliegenden Mechanismen des modellierten Systems besser erfassen können. Wir werden uns mit Adaptive skip intervals (ASI) befassen; eine Methode, die es dynamischen Modellen ermöglicht, ihre eigene zeitliche Vergröberung an jedem Punkt zu wählen. Dadurch werden langfristige Vorhersagen sowohl einfacher als auch rechnerisch effizienter. Als Nächstes werden wir uns mit alternativen Möglichkeiten zur Aggregation von Gradienten in verschiedenen Umgebungen befassen, was zum Begriff der Invariant Learning Consistency (ILC) und der Methode AND-mask für einen modifizierten stochastischen Gradientenabstieg führt. Durch das Herausfiltern inkonsistenter Trainingssignale aus verschiedenen Umgebungen bleiben die gemeinsamen Mechanismen erhalten. Schließlich werden wir sehen, dass Lernen auf der Grundlage von Meta-Gradienten Trajektorien von dynamischen Systemen transformieren kann, um nützliche Lernsignale in Richtung eines zugrunde liegenden Ziels zu konstruieren, wie z. B. Reward beim Reinforcement Learning. Dadurch kann das interne Modell sowohl eine zeitliche als auch eine Zustandsabstraktion beinhalten

Learning relational models with human interaction for planning in robotics

Automated planning has proven to be useful to solve problems where an agent has to maximize a reward function by executing actions. As planners have been improved to salve more expressive and difficult problems, there is an increasing interest in using planning to improve efficiency in robotic tasks. However, planners rely on a domain model, which has to be either handcrafted or learned. Although learning domain models can be very costly, recent approaches provide generalization capabilities and integrate human feedback to reduce the amount of experiences required to learn. In this thesis we propase new methods that allow an agent with no previous knowledge to solve certain problems more efficiently by using task planning. First, we show how to apply probabilistic planning to improve robot performance in manipulation tasks (such as cleaning the dirt or clearing the tableware on a table). Planners obtain sequences of actions that get the best result in the long term, beating reactive strategies. Second, we introduce new reinforcement learning algorithms where the agent can actively request demonstrations from a teacher to learn new actions and speed up the learning process. In particular, we propase an algorithm that allows the user to set the mínimum quality to be achieved, where a better quality also implies that a larger number of demonstrations will be requested . Moreover, the learned model is analyzed to extract the unlearned or problematic parts of the model. This information allow the agent to provide guidance to the teacher when a demonstration is requested, and to avoid irrecoverable errors. Finally, a new domain model learner is introduced that, in addition to relational probabilistic action models, can also learn exogenous effects. This learner can be integrated with existing planners and reinforcement learning algorithms to salve a wide range of problems. In summary, we improve the use of learning and task planning to salve unknown tasks. The improvements allow an agent to obtain a larger benefit from planners, learn faster, balance the number of action executions and teacher demonstrations, avoid irrecoverable errors, interact with a teacher to solve difficult problems, and adapt to the behavior of other agents by learning their dynamics. All the proposed methods were compared with state-of-the-art approaches, and were also demonstrated in different scenarios, including challenging robotic tasks.La planificación automática ha probado ser de gran utilidad para resolver problemas en los que un agente tiene que ejecutar acciones para maximizar una función de recompensa. A medida que los planificadores han sido capaces de resolver problemas cada vez más complejos, ha habido un creciente interés por utilizar dichos planificadores para mejorar la eficiencia de tareas robóticas. Sin embargo, los planificadores requieren un modelo del dominio, el cual puede ser creado a mano o aprendido. Aunque aprender modelos automáticamente puede ser costoso, recientemente han aparecido métodos que permiten la interacción persona-máquina y generalizan el conocimiento para reducir la cantidad de experiencias requeridas para aprender. En esta tesis proponemos nuevos métodos que permiten a un agente sin conocimiento previo de la tarea resolver problemas de forma más eficiente mediante el uso de planificación automática. Comenzaremos mostrando cómo aplicar planificación probabilística para mejorar la eficiencia de robots en tareas de manipulación (como limpiar suciedad o recoger una mesa). Los planificadores son capaces de obtener las secuencias de acciones que producen los mejores resultados a largo plazo, superando a las estrategias reactivas. Por otro lado, presentamos nuevos algoritmos de aprendizaje por refuerzo en los que el agente puede solicitar demostraciones a un profesor. Dichas demostraciones permiten al agente acelerar el aprendizaje o aprender nuevas acciones. En particular, proponemos un algoritmo que permite al usuario establecer la mínima suma de recompensas que es aceptable obtener, donde una recompensa más alta implica que se requerirán más demostraciones. Además, el modelo aprendido será analizado para identificar qué partes están incompletas o son problemáticas. Esta información permitirá al agente evitar errores irrecuperables y también guiar al profesor cuando se solicite una demostración. Finalmente, se ha introducido un nuevo método de aprendizaje para modelos de dominios que, además de obtener modelos relacionales de acciones probabilísticas, también puede aprender efectos exógenos. Mostraremos cómo integrar este método en algoritmos de aprendizaje por refuerzo para poder abordar una mayor cantidad de problemas. En resumen, hemos mejorado el uso de técnicas de aprendizaje y planificación para resolver tareas desconocidas a priori. Estas mejoras permiten a un agente aprovechar mejor los planificadores, aprender más rápido, elegir entre reducir el número de acciones ejecutadas o el número de demostraciones solicitadas, evitar errores irrecuperables, interactuar con un profesor para resolver problemas complejos, y adaptarse al comportamiento de otros agentes aprendiendo sus dinámicas. Todos los métodos propuestos han sido comparados con trabajos del estado del arte, y han sido evaluados en distintos escenarios, incluyendo tareas robóticas

Grounded Semantic Reasoning for Robotic Interaction with Real-World Objects

Robots are increasingly transitioning from specialized, single-task machines to general-purpose systems that operate in unstructured environments, such as homes, offices, and warehouses. In these real-world domains, robots need to manipulate novel objects while adapting to changes in environments and goals. Semantic knowledge, which concisely describes target domains with symbols, can potentially reveal the meaningful patterns shared between problems and environments. However, existing robots are yet to effectively reason about semantic data encoding complex relational knowledge or jointly reason about symbolic semantic data and multimodal data pertinent to robotic manipulation (e.g., object point clouds, 6-DoF poses, and attributes detected with multimodal sensing). This dissertation develops semantic reasoning frameworks capable of modeling complex semantic knowledge grounded in robot perception and action. We show that grounded semantic reasoning enables robots to more effectively perceive, model, and interact with objects in real-world environments. Specifically, this dissertation makes the following contributions: (1) a survey providing a unified view for the diversity of works in the field by formulating semantic reasoning as the integration of knowledge sources, computational frameworks, and world representations; (2) a method for predicting missing relations in large-scale knowledge graphs by leveraging type hierarchies of entities, effectively avoiding ambiguity while maintaining generalization of multi-hop reasoning patterns; (3) a method for predicting unknown properties of objects in various environmental contexts, outperforming prior knowledge graph and statistical relational learning methods due to the use of n-ary relations for modeling object properties; (4) a method for purposeful robotic grasping that accounts for a broad range of contexts (including object visual affordance, material, state, and task constraint), outperforming existing approaches in novel contexts and for unknown objects; (5) a systematic investigation into the generalization of task-oriented grasping that includes a benchmark dataset of 250k grasps, and a novel graph neural network that incorporates semantic relations into end-to-end learning of 6-DoF grasps; (6) a method for rearranging novel objects into semantically meaningful spatial structures based on high-level language instructions, more effectively capturing multi-object spatial constraints than existing pairwise spatial representations; (7) a novel planning-inspired approach that iteratively optimizes placements of partially observed objects subject to both physical constraints and semantic constraints inferred from language instructions.Ph.D

A Survey of Large Language Models

Language is essentially a complex, intricate system of human expressions governed by grammatical rules. It poses a significant challenge to develop capable AI algorithms for comprehending and grasping a language. As a major approach, language modeling has been widely studied for language understanding and generation in the past two decades, evolving from statistical language models to neural language models. Recently, pre-trained language models (PLMs) have been proposed by pre-training Transformer models over large-scale corpora, showing strong capabilities in solving various NLP tasks. Since researchers have found that model scaling can lead to performance improvement, they further study the scaling effect by increasing the model size to an even larger size. Interestingly, when the parameter scale exceeds a certain level, these enlarged language models not only achieve a significant performance improvement but also show some special abilities that are not present in small-scale language models. To discriminate the difference in parameter scale, the research community has coined the term large language models (LLM) for the PLMs of significant size. Recently, the research on LLMs has been largely advanced by both academia and industry, and a remarkable progress is the launch of ChatGPT, which has attracted widespread attention from society. The technical evolution of LLMs has been making an important impact on the entire AI community, which would revolutionize the way how we develop and use AI algorithms. In this survey, we review the recent advances of LLMs by introducing the background, key findings, and mainstream techniques. In particular, we focus on four major aspects of LLMs, namely pre-training, adaptation tuning, utilization, and capacity evaluation. Besides, we also summarize the available resources for developing LLMs and discuss the remaining issues for future directions.Comment: ongoing work; 51 page

Intelligent Systems

This book is dedicated to intelligent systems of broad-spectrum application, such as personal and social biosafety or use of intelligent sensory micro-nanosystems such as "e-nose", "e-tongue" and "e-eye". In addition to that, effective acquiring information, knowledge management and improved knowledge transfer in any media, as well as modeling its information content using meta-and hyper heuristics and semantic reasoning all benefit from the systems covered in this book. Intelligent systems can also be applied in education and generating the intelligent distributed eLearning architecture, as well as in a large number of technical fields, such as industrial design, manufacturing and utilization, e.g., in precision agriculture, cartography, electric power distribution systems, intelligent building management systems, drilling operations etc. Furthermore, decision making using fuzzy logic models, computational recognition of comprehension uncertainty and the joint synthesis of goals and means of intelligent behavior biosystems, as well as diagnostic and human support in the healthcare environment have also been made easier