MaestROB: A Robotics Framework for Integrated Orchestration of Low-Level Control and High-Level Reasoning

This paper describes a framework called MaestROB. It is designed to make the robots perform complex tasks with high precision by simple high-level instructions given by natural language or demonstration. To realize this, it handles a hierarchical structure by using the knowledge stored in the forms of ontology and rules for bridging among different levels of instructions. Accordingly, the framework has multiple layers of processing components; perception and actuation control at the low level, symbolic planner and Watson APIs for cognitive capabilities and semantic understanding, and orchestration of these components by a new open source robot middleware called Project Intu at its core. We show how this framework can be used in a complex scenario where multiple actors (human, a communication robot, and an industrial robot) collaborate to perform a common industrial task. Human teaches an assembly task to Pepper (a humanoid robot from SoftBank Robotics) using natural language conversation and demonstration. Our framework helps Pepper perceive the human demonstration and generate a sequence of actions for UR5 (collaborative robot arm from Universal Robots), which ultimately performs the assembly (e.g. insertion) task.Comment: IEEE International Conference on Robotics and Automation (ICRA) 2018. Video: https://www.youtube.com/watch?v=19JsdZi0TW

Task analysis of discrete and continuous skills: a dual methodology approach to human skills capture for automation

There is a growing requirement within the field of intelligent automation for a formal methodology to capture and classify explicit and tacit skills deployed by operators during complex task performance. This paper describes the development of a dual methodology approach which recognises the inherent differences between continuous tasks and discrete tasks and which proposes separate methodologies for each. Both methodologies emphasise capturing operators’ physical, perceptual, and cognitive skills, however, they fundamentally differ in their approach. The continuous task analysis recognises the non-arbitrary nature of operation ordering and that identifying suitable cues for subtask is a vital component of the skill. Discrete task analysis is a more traditional, chronologically ordered methodology and is intended to increase the resolution of skill classification and be practical for assessing complex tasks involving multiple unique subtasks through the use of taxonomy of generic actions for physical, perceptual, and cognitive actions

Conclusions and implications of automation in space

Space facilities and programs are reviewed. Space program planning is discussed

The development of a Human Factors Readiness Level tool for implementing industrial human-robot collaboration

The concept of industrial human-robot collaboration (HRC) is becoming increasingly attractive as a means for enhancing manufacturing productivity and product. However, due to traditional preventive health and safety standards, there have been few operational examples of true HRC, so it has not been possible to explore the organisational human factors that need to be considered by manufacturing organisations to realise the benefits of industrial HRC until recently. Charalambous, Fletcher and Webb (2015) made the first attempt to identify the key organisational human factors for the successful implementation of industrial HRC through an industrial exploratory case study. This work enabled (i) development of a theoretical framework of key organisational human factors relevant to industrial HRC and (ii) identification of these factors as enablers or barriers. Although identifying the key organisational human factors (HF) was an important step, it presented a crucial question: when should practitioners involved in HRC design and implementation consider these factors? New industrial processes are typically designed and implemented using a maturity or readiness evaluation system, but these do not incorporate of or link to any formal considerations of HF. The aim of this paper is to expand on the previous findings and link the key human factors in the theoretical framework directly to a recognised industrial maturity readiness level system to develop a new Human Factors Readiness Level (HFRL) tool for system design practitioners to optimise successful implementation of industrial HRC

The NWRA Classification Infrastructure: Description and Extension to the Discriminant Analysis Flare Forecasting System (DAFFS)

A classification infrastructure built upon Discriminant Analysis has been developed at NorthWest Research Associates for examining the statistical differences between samples of two known populations. Originating to examine the physical differences between flare-quiet and flare-imminent solar active regions, we describe herein some details of the infrastructure including: parametrization of large datasets, schemes for handling "null" and "bad" data in multi-parameter analysis, application of non-parametric multi-dimensional Discriminant Analysis, an extension through Bayes' theorem to probabilistic classification, and methods invoked for evaluating classifier success. The classifier infrastructure is applicable to a wide range of scientific questions in solar physics. We demonstrate its application to the question of distinguishing flare-imminent from flare-quiet solar active regions, updating results from the original publications that were based on different data and much smaller sample sizes. Finally, as a demonstration of "Research to Operations" efforts in the space-weather forecasting context, we present the Discriminant Analysis Flare Forecasting System (DAFFS), a near-real-time operationally-running solar flare forecasting tool that was developed from the research-directed infrastructure.Comment: J. Space Weather Space Climate: Accepted / in press; access supplementary materials through journal; some figures are less than full resolution for arXi

An Intelligent Robot and Augmented Reality Instruction System

Human-Centered Robotics (HCR) is a research area that focuses on how robots can empower people to live safer, simpler, and more independent lives. In this dissertation, I present a combination of two technologies to deliver human-centric solutions to an important population. The first nascent area that I investigate is the creation of an Intelligent Robot Instructor (IRI) as a learning and instruction tool for human pupils. The second technology is the use of augmented reality (AR) to create an Augmented Reality Instruction (ARI) system to provide instruction via a wearable interface. To function in an intelligent and context-aware manner, both systems require the ability to reason about their perception of the environment and make appropriate decisions. In this work, I construct a novel formulation of several education methodologies, particularly those known as response prompting, as part of a cognitive framework to create a system for intelligent instruction, and compare these methodologies in the context of intelligent decision making using both technologies. The IRI system is demonstrated through experiments with a humanoid robot that uses object recognition and localization for perception and interacts with students through speech, gestures, and object interaction. The ARI system uses augmented reality, computer vision, and machine learning methods to create an intelligent, contextually aware instructional system. By using AR to teach prerequisite skills that lend themselves well to visual, augmented reality instruction prior to a robot instructor teaching skills that lend themselves to embodied interaction, I am able to demonstrate the potential of each system independently as well as in combination to facilitate students\u27 learning. I identify people with intellectual and developmental disabilities (I/DD) as a particularly significant use case and show that IRI and ARI systems can help fulfill the compelling need to develop tools and strategies for people with I/DD. I present results that demonstrate both systems can be used independently by students with I/DD to quickly and easily acquire the skills required for performance of relevant vocational tasks. This is the first successful real-world application of response-prompting for decision making in a robotic and augmented reality intelligent instruction system

Design Concepts for Automating Maintenance Instructions

This research task was performed under the Technology for Readiness and Sustainment (TRS) contract (F33615-99-D-6001) for the Air Force Research Laboratory (AFRL), Sustainment Logistics Branch (HESS) at Wright-Patterson AFB, OH. The period of performance spanned one year starting 29 January 1999. The objective of this task was to develop and demonstrate a framework that can support the automated validation and verification of aircraft maintenance Technical Orders (TOs). The research team examined all stages ofTO generation to determine which tasks most warranted further research. From that investigation, validation and verification of appropriate, safe, and correct procedure steps emerged as the primary research target. This process would be based on available computer-aided design (CAD) data, procedure step ordering from existing sources, and human models. This determination was based on which tasks could yield the greatest impact on the authoring process and offer the greatest potential economic benefits. The team then developed a research roadmap and outlined specific technologies to be addressed in possible subsequent Air Force research tasks. To focus on the potential technology integration of the validation and verification component into existing or future TO generation procedures, we defined a demonstration scenario. Using the Front Uplock Hook assembly from an F/A-18 as the subject, we examined task procedure steps and failures that could be exposed by automated validation tools. These included hazards to personnel, damage to equipment, and incorrect disassembly order. Using the Parameterized Action Representation (PAR) developed on previous projects for actions and equipment behaviors, we characterized procedure steps and their positive and negative consequences. Finally, we illustrated a hypothetical user interface extension to a typical Interactive Electronic Technical Manual (IETM) authoring system to demonstrate how this process might appear to the TO author

Technology assessment of advanced automation for space missions

Six general classes of technology requirements derived during the mission definition phase of the study were identified as having maximum importance and urgency, including autonomous world model based information systems, learning and hypothesis formation, natural language and other man-machine communication, space manufacturing, teleoperators and robot systems, and computer science and technology