Adaptive Perception for Efficient Spatio-temporal Language Grounding in Dynamic Environments - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Adaptive Perception for Efficient Spatio-temporal Language Grounding in Dynamic Environments write by Siddharth Patki. This book was released on 2023. Adaptive Perception for Efficient Spatio-temporal Language Grounding in Dynamic Environments available in PDF, EPUB and Kindle. "As robots are becoming increasingly prevalent in shared spaces such as homes and offices, the need for efficient and effective human-robot collaboration has become imperative. One of the key aspects of human-robot collaboration is the ability of robots to understand and interpret human instructions. However, the challenges associated with understanding natural language instructions in cluttered and dynamic environments remain significant. Recent approaches to grounded language understanding reason only in the context of an instantaneous state of the world. Though this allows for interpreting a variety of utterances in the current context of the world, these models fail to interpret utterances which require the knowledge of past dynamics of the world, thereby hindering effective human-robot collaboration in dynamic environments. Extending the contemporary models to reason about utterances which require knowledge of the past dynamics of the world introduces non-trivial challenges related to to both world state estimation and symbol grounding. Specifically in relation to world modeling, constructing a comprehensive model of the dynamic world that tracks the states of all objects in the robot's workspace is computationally intractable and difficult to scale with increasing clutter in the environments. On the other hand, a poorly detailed model of the environment limits the diversity of the utterances that can be interpreted and executed. A fundamental research question then is, how to efficiently reason over this rich information in a manner that enables robots to efficiently execute a variety of instructions in highly cluttered and dynamic worlds. In this thesis I present an arc of research which investigates how the information in language can be utilized to construct task-specific representations of the world, enabling faster and more accurate symbol grounding in cluttered and dynamic environments. Specifically, first this thesis presents a learned model of language and perception called Language Guided Adaptive Perception (LG-AP) that allows language to steer the interpretation of raw observations to create world models that are minimal but sufficient for the grounding robot instructions in static environments. Second, this thesis presents a novel approach called Language Guided Temporally Adaptive Perception (LG-TAP), that facilitates the construction of temporally compact models of dynamic worlds through closed-loop grounding and perception. This document includes is a discussion of the synergies that exist among these contributions and how adapting perception by exploiting the information in language can improve runtime efficiency and accuracy of robot instruction following in static and dynamic environments with high degree of clutter."--Pages xii-xiii.
Spatial Biases in Perception and Cognition
Spatial Biases in Perception and Cognition - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Spatial Biases in Perception and Cognition write by Timothy L. Hubbard. This book was released on 2018-08-23. Spatial Biases in Perception and Cognition available in PDF, EPUB and Kindle. Numerous spatial biases influence navigation, interactions, and preferences in our environment. This volume considers their influences on perception and memory.
Sensor Fusion and Decentralized Control in Robotic Systems
Sensor Fusion and Decentralized Control in Robotic Systems - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Sensor Fusion and Decentralized Control in Robotic Systems write by . This book was released on 2000. Sensor Fusion and Decentralized Control in Robotic Systems available in PDF, EPUB and Kindle.
Cross-Modal Learning: Adaptivity, Prediction and Interaction
Cross-Modal Learning: Adaptivity, Prediction and Interaction - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Cross-Modal Learning: Adaptivity, Prediction and Interaction write by Jianwei Zhang. This book was released on 2023-02-02. Cross-Modal Learning: Adaptivity, Prediction and Interaction available in PDF, EPUB and Kindle. The purpose of this Research Topic is to reflect and discuss links between neuroscience, psychology, computer science and robotics with regards to the topic of cross-modal learning which has, in recent years, emerged as a new area of interdisciplinary research. The term cross-modal learning refers to the synergistic synthesis of information from multiple sensory modalities such that the learning that occurs within any individual sensory modality can be enhanced with information from one or more other modalities. Cross-modal learning is a crucial component of adaptive behavior in a continuously changing world, and examples are ubiquitous, such as: learning to grasp and manipulate objects; learning to walk; learning to read and write; learning to understand language and its referents; etc. In all these examples, visual, auditory, somatosensory or other modalities have to be integrated, and learning must be cross-modal. In fact, the broad range of acquired human skills are cross-modal, and many of the most advanced human capabilities, such as those involved in social cognition, require learning from the richest combinations of cross-modal information. In contrast, even the very best systems in Artificial Intelligence (AI) and robotics have taken only tiny steps in this direction. Building a system that composes a global perspective from multiple distinct sources, types of data, and sensory modalities is a grand challenge of AI, yet it is specific enough that it can be studied quite rigorously and in such detail that the prospect for deep insights into these mechanisms is quite plausible in the near term. Cross-modal learning is a broad, interdisciplinary topic that has not yet coalesced into a single, unified field. Instead, there are many separate fields, each tackling the concerns of cross-modal learning from its own perspective, with currently little overlap. We anticipate an accelerating trend towards integration of these areas and we intend to contribute to that integration. By focusing on cross-modal learning, the proposed Research Topic can bring together recent progress in artificial intelligence, robotics, psychology and neuroscience.
Federated Learning
Federated Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Federated Learning write by Qiang Yang. This book was released on 2020-11-25. Federated Learning available in PDF, EPUB and Kindle. This book provides a comprehensive and self-contained introduction to federated learning, ranging from the basic knowledge and theories to various key applications. Privacy and incentive issues are the focus of this book. It is timely as federated learning is becoming popular after the release of the General Data Protection Regulation (GDPR). Since federated learning aims to enable a machine model to be collaboratively trained without each party exposing private data to others. This setting adheres to regulatory requirements of data privacy protection such as GDPR. This book contains three main parts. Firstly, it introduces different privacy-preserving methods for protecting a federated learning model against different types of attacks such as data leakage and/or data poisoning. Secondly, the book presents incentive mechanisms which aim to encourage individuals to participate in the federated learning ecosystems. Last but not least, this book also describes how federated learning can be applied in industry and business to address data silo and privacy-preserving problems. The book is intended for readers from both the academia and the industry, who would like to learn about federated learning, practice its implementation, and apply it in their own business. Readers are expected to have some basic understanding of linear algebra, calculus, and neural network. Additionally, domain knowledge in FinTech and marketing would be helpful.”
