Social Signals for Interactive, Error Aware Robotic Systems

Robot errors during human-robot interaction are inescapable; they can occur during any task and do not necessarily fit human expectations. Left unmanaged, they harm task performance and user trust, resulting in user unwillingness to work with a robot. Previous error detection techniques have used task or error specific information for robust management and can lack the versatility to address robot errors across tasks and error types. In this dissertation, I leverage natural human responses to robot errors in physical HRI for error detection across task, scenario, and error type for flexible robot error detection. My approach is two-fold: (1) understand and model social signals (facial action units, or AUs) in response to unexpected robot errors and (2) use these models for reliable, flexible automatic error detection. First, I explore how users respond to unexpected robot errors and if social signals can be effectively used to detect errors across individuals in Programming by Demonstration scenarios. I then expand my research to collaborative human-robot tasks by generating and analyzing a dataset derived from three distinct HRI studies to understand the prevalence of social signals across different errors, tasks, and scenarios. Next, I explore the impact of context on social signals for detection and investigate how people react to robot errors in-the-wild. From this set of four studies, I demonstrate that natural AUs can be used to detect and temporally localize errors with reasonable accuracy and timeliness across different tasks, error types, and people. Recognizing the limitations of relying solely on AUs, I move towards a multimodal approach. I propose a conceptual framework for error awareness that takes a multimodal approach using social signals to provide error detection flexibility. Finally, I investigate the benefits of proactive error detection using social signals based upon my error-aware framework. Overall, results from my work show that in physical HRI, social signals exhibited in response to robot errors are good indicators and enable flexible automatic error detection. This dissertation contributes to our knowledge of how people implicitly react to robot errors and how behavioral signals can used for flexible robot error detection across error types and scenarios

Social Signals for Interactive, Error Aware Robotic Systems

Robot errors during human-robot interaction are inescapable; they can occur during any task and do not necessarily fit human expectations. Left unmanaged, they harm task performance and user trust, resulting in user unwillingness to work with a robot. Previous error detection techniques have used task or error specific information for robust management and can lack the versatility to address robot errors across tasks and error types. In this dissertation, I leverage natural human responses to robot errors in physical HRI for error detection across task, scenario, and error type for flexible robot error detection. My approach is two-fold: (1) understand and model social signals (facial action units, or AUs) in response to unexpected robot errors and (2) use these models for reliable, flexible automatic error detection. First, I explore how users respond to unexpected robot errors and if social signals can be effectively used to detect errors across individuals in Programming by Demonstration scenarios. I then expand my research to collaborative human-robot tasks by generating and analyzing a dataset derived from three distinct HRI studies to understand the prevalence of social signals across different errors, tasks, and scenarios. Next, I explore the impact of context on social signals for detection and investigate how people react to robot errors in-the-wild. From this set of four studies, I demonstrate that natural AUs can be used to detect and temporally localize errors with reasonable accuracy and timeliness across different tasks, error types, and people. Recognizing the limitations of relying solely on AUs, I move towards a multimodal approach. I propose a conceptual framework for error awareness that takes a multimodal approach using social signals to provide error detection flexibility. Finally, I investigate the benefits of proactive error detection using social signals based upon my error-aware framework. Overall, results from my work show that in physical HRI, social signals exhibited in response to robot errors are good indicators and enable flexible automatic error detection. This dissertation contributes to our knowledge of how people implicitly react to robot errors and how behavioral signals can used for flexible robot error detection across error types and scenarios

Pre-Industrial Western Printing Inks, c.1450-1850

It is often assumed that all printing inks are the same: black, inert, and stable over centuries. However, the ingredients in their recipes (or, as measurements were standardised in the 18th century, the components in their formulation) have varied enormously since their invention in the mid-1400s. This chapter offers a starting point for understanding the kind of pre-industrial oil-based printing inks that were commonly used for printing texts, images, music, and other kinds of content, mainly on paper and parchment supports, in a printing press, first in Western Europe and Central and Eastern Europe, areas under European colonial control, and other areas as the printing press spread. (Block-printing in Europe and relief printing in Asia conventionally uses a water-based ink.) This study begins with the invention of printing ink c.1450, when Johannes Gutenberg (d. 1468) created black printing ink for his first publications, and red printing ink c.1455 for the Gutenberg Bible. It ends with transformation of its production due to industrialisation and the development of synthetic constituents around1850. Relief, intaglio, and planographic (i.e. lithographic) printing inks had different pathways to industrialisation, but 1850 is a broadly indicative turning point because this timespan largely overlaps with the handpress period, c.1450–1830. Given the paucity of literature on this topic, it is believed that this chapter is the most substantial survey of the materiality of pre-industrial printing inks to date

Graph-based Modeling and Simulation of Emergency Services Communication Systems

Emergency Services Communication Systems (ESCS) are evolving into Internet Protocol based communication networks, promising enhancements to their function, availability, and resilience. This increase in complexity and cyber-attack surface demands better understanding of these systems\u27 breakdown dynamics under extreme circumstances. Existing ESCS research largely overlooks simulation and the little work that exists focuses primarily on cybersecurity threats and neglects critical factors such as non-stationarity of call arrivals. This paper introduces a robust, adaptable graph-based simulation framework and essential mathematical models for ESCS simulation. The framework uses a representation of ESCSes where each vertex is a communicating finite-state machine that exchanges messages along edges and whose behavior is governed by a discrete event queuing model. Call arrival burstiness and its connection to emergency incidents is modeled through a cluster point process. Model applicability is demonstrated through simulations of the Seattle Police Department ESCS. Ongoing work is developing GPU implementation and exploring use in cybersecurity tabletop exercises.8 pages, 3 figure

Alternative splicing converts STIM2 from an activator to an inhibitor of store-operated calcium channels

Store-operated calcium entry (SOCE) regulates a wide variety of essential cellular functions. SOCE is mediated by STIM1 and STIM2, which sense depletion of ER Ca2+ stores and activate Orai channels in the plasma membrane. Although the amplitude and dynamics of SOCE are considered important determinants of Ca2+-dependent responses, the underlying modulatory mechanisms are unclear. In this paper, we identify STIM2??, a highly conserved alternatively spliced isoform of STIM2, which, in contrast to all known STIM isoforms, is a potent inhibitor of SOCE. Although STIM2?? does not by itself strongly bind Orai1, it is recruited to Orai1 channels by forming heterodimers with other STIM isoforms. Analysis of STIM2?? mutants and Orai1-STIM2?? chimeras suggested that it actively inhibits SOCE through a sequence-specific allosteric interaction with Orai1. Our results reveal a previously unrecognized functional flexibility in the STIM protein family by which alternative splicing creates negative and positive regulators of SOCE to shape the amplitude and dynamics of Ca2+ signals.open

Racing for green hydrogen economics with polymer electrolyte water electrolysis : how to be achieved

For renewable hydrogen production, polymer electrolyte membrane water electrolysis is the most promising technology. However, the technology is not yet competitive with conventional hydrogen production in terms of cost. The impact of cost reduction options on CAPEX and OPEX is investigated. Depending on the hours of operation, the main cost factor is the production and manufacturing of components or the price of electricity. Clearly a tremendous need to implement low‐cost electrolysis cells on a large scale to bring green hydrogen production costs to a level 1-3 € kg-1 hydrogen

A Protocol for the Development, Evaluation and Application of Environmental Models in Decision Making

Models have emerged as essential tools in environmental management, whether used to further the understanding of complex environmental processes or to inform decisions for environmental planning, remediation, protection or regulation. However, their utility aside, there is also an acknowledgment of their limitations. The question is not whether or not to use models, but rather how best to develop and use models to arrive at credible, defensible and robust decisions and what attributes make a model useful for a given situation. To understand the role of models and decision support tools in environmental management, we must first consider the different types of decisions made, particularly within a regulatory or policy-making context and the different decision-making contexts and processes. This paper will explore the requirements for effective model-based decision support as well as the role that characterizing and communicating uncertainty plays in influencing the utility of the use of models in environmental decision making. The paper will also build upon the recent work of the Council of Regulatory Environmental Modeling of the US Environmental Protection Agency to identify the major guiding principles for effective model development, evaluation and use to inform environmental management decisions and policy