ABSTRACT

Automation, foreign competition, and the increasing use of robots replacing human jobs, stress the need for a major shift in vocational training practices to training for intelligent manufacturing environments, so-called "Industry 4.0". In particular, vocational safety training using the latest robot and other technologies is imperative, as thousands of workers lose their job or die on the job each year due to accidents, unforeseen injuries, and lack of appropriate assessment and training. The objective of this Partnerships for Innovation: Building Innovation Capacity (PFI:BIC) project is to develop iWork, a smart robot-based vocational assessment and intervention system to assess the physical, cognitive and collaboration skills of an industry worker while he/she performs a manufacturing tasks in a simulated industry setting and collaborating with a robot to do the task. The aim is to transform traditional vocational training and rehabilitation practices to an evidence-based and personalized system that can be used to (re)train, retain, and prepare workers for robotic factories of the future. The need for personalized vocational training, rehabilitation and accurate job-matching is essential to ensuring a strong manufacturing sector, vital to America's economic development and ability to innovate. The iWork service is "smart" because it can adjust and adapt to the individual's abilities as it assesses him/her and help decide on the type of tasks needed to test and train, based on the job's complexity, difficulty or familiarity to the worker. The iWork system integrates human expert knowledge to overcome or compensate for detected worker constraints.
Research has shown that robot trainers can increase motivation and sustain interest, increase compliance and learning, and provide training for specific and individual needs. The iWork system aims to assess and train both the human and the work-assistive robot, as they collaborate on a manufacturing job. The projected outcome is low-cost vocational training solutions that can have substantial economic and societal benefits to diverse economic sectors. Most importantly, if successful, projected outcomes could impact how millions of persons seeking a manufacturing job are trained, including those facing a type of learning, physical or aging disability. The system's mobile, low cost methods accelerate recognizing a worker's specific needs and improve the ability of the vocational expert to make correlations between cognitive and physical assessments, thus empowering traditional practices with user-centric targeted training methods. In addition, the project's robot-based emphasis on safety and risk assessment, can reduce liability costs and productivity setbacks faced by industry, due to manufacturing accidents.
The iWork system uses computational methods in reinforcement (machine) learning, data mining, collaborative filtering and human robot interaction to collect and analyze multi-sensing worker data during a manufacturing human-robot collaboration simulation. Data collected and analyzed come from sensors, wearables, and explicit user feedback measuring worker movements, eye gazes, errors made, performance delays, human-robot interactions, physiological metrics, and others, depending on the task. The system has a closed loop architecture composed of four phases: assessment, recommendation, intervention (or adjustment), and evaluation, with a human expert in the loop. The system generates recommendations for personalized interventions to the expert, at different loop intervals. Use of the latest developments in sensing technologies, robotics and intelligent communications, assess the ability to enhance the intelligence of a robot co-worker with more human-like learning and collaboration abilities to support the human in achieving a task. The system is modular and customizable to a particular manufacturing task, domain or worker robot. Two types of robots are used, socially assistive robots that provide non-contact user assistance through feedback and physically assistive robots that provide cognitive, physical and collaboration skill training. To predict risks of injury due to inattention, age, vision, or physical and mental issues, motion analysis and kinematics experiments are conducted to determine the type of safety training needed, to assess how well a human interacts with a collaborative robot, and how best to train the robot to help the human overcome identified physical and other deficiencies in performing a given task. The project integrates three main areas of expertise, engineered service system design, where assistive robots interact with and train each other to collaborate; computing, sensing, and information technologies, where machine learning, data mining and recommender algorithms are used to identify behavioral patterns of interest, and recommend targeted interventions; and human factors and cognitive engineering that deploy methods from the team's expertise in workplace assessment, personalized psychiatric intervention, and evaluation methods of vocational satisfaction, work habits, work quality, etc., as they relate to job preparation and retention.
The project has an interdisciplinary team of experts from two collaborating universities, University of Texas Arlington (UTA) and Yale University, representing several fields, including human factors, psychology, computing, and industrial organization. The project deploys two primary industry partners, SoftBank Robotics (San Francisco, CA) manufacturer of humanoid service robots, and InteraXon (Canada), producing mobile EEG devices, who provides hardware, software and know-how to enhance iWork's functionality in cognitive activity monitoring. The broader context partners include, C8Sciences (USA), Assistive Technology Resources (USA), Barrett Technologies Inc. (USA), and the Dallas Veteran Affairs Research Corp. (USA).

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