Training as an Initial Use Case for Augmented Reality

Among use cases to which Augmented Reality brings value, an increasing portion focuses on training. Training use cases fill an immediate need for user education in a rapidly changing industrial environment and can be successfully implemented with current AR technologies.

Training requirements differ across industries and employee roles. Metrics with which organizations can track training benefits also vary widely. AR-assisted on-the-job or classroom training of assembly and maintenance personnel can ultimately reduce the cost and time of doing tasks, leading to higher operational efficiency.


Some manual production tasks such as spot welding and riveting are still prevalent despite the fact that many assembly workflows and procedures are automated. These manual tasks may require not only particular skills to fulfill them, but also demand expertise in a particular area of assembly, as well as personal adaptability to changing production processes.

Agile manufacturing is particularly demanding on workers, as the high cost of modifying production environments means that it often falls to humans to remain flexible in the face of changes. The less automatization and more customization in production environments, the more experienced workers need to consult manuals, work instructions and fellow experts to perform challenging tasks, which in turn can effectively become problem-solving exercises. This puts greater emphasis on training.


Training is also important in maintenance organizations. While many aspects of preventive maintenance have become automated via data collection, some types of corrective interventions require resolution by experts, who might be off site. Redundant systems may be available to provide fail-safe operation and minimize risk of service degradation but human intervention is usually still necessary. Whether preventative or prescribed, assessing risks, organizing a task force and scheduling the intervention according to escalation protocols take time.

Training for system maintenance in order to minimize downtime is a significant endeavor as well as time consuming, especially in cases requiring certification and where an employee’s equipment service credentials must be maintained with repeated training. Often, cross training is necessary to acquire expert knowledge for diagnosing a variety of problems.

Current Technological Limitations

Despite their potential, Augmented Reality technologies are still in their infancy. Mobile devices well-suited to AR-assisted tasks, such as Amazon’s Fire Phone and Google’s upcoming Tango Project, are making their way to the mass market. However, in a shop floor or factory setting, the acceptance and implementation of AR faces significant barriers:

  • Workplaces are often dynamic environments, with frequent repositioning and changing of tool and equipment layouts
  • Factory floors are often fully utilized with little open space
  • Natural light enters the workplace and its intensity and angles change over the course of the day and the seasons

These realities pose challenges for AR implementations in various ways. For example:

  • Technologies for object tracking and image recognition in mainline workflows are not optimized. Although edge-based and markerless tracking capabilities are available in Augmented Reality SDKs, they are not sufficiently powerful or flexible for tracking reliably in all conditions. For example, edge-based tracking can work well under varying lighting conditions, but is still susceptible to engineered surfaces presenting repetitive edges or many smooth surfaces.
  • Marker-based tracking can produce consistent results, but fiducial position and quality in changing and cluttered work environments is difficult to maintain or guarantee.
  • Capital investment and costs for customized development of pilot projects can be high. For example, prices for dedicated AR devices possessing novel features such as multiple cameras or sensors for depth perception, sufficient processing for real time capture of camera pose in a 3D coordinate system and realistic rendering based on image-based lighting are correspondingly steep. Customization requirements such as ruggedization for specific business environments must often be added to the cost.
  • As a result of technological shortcomings in features such as tracking, some companies must explore and develop their own solutions, such as employing multiple cameras or sensors in a “six degrees of freedom” configuration in a controlled environment.
  • Form factors and user interfaces of available head-mounted displays that are powerful enough for Augmented Reality tend to be bulky and provide an immersive experience. This can also create safety risks in busy and cluttered environments.

Although such factors introduce barriers to mainstream adoption of AR in enterprise, many companies are developing pilot projects to assess benefits at a reasonable cost. Such projects are being implemented in controlled environments, which provide opportunities to introduce AR-based training programs.

AR-based Training for Efficient Learning

Well-designed, AR-based training procedures using objects and environments that are as close as possible to real experiences can enhance learning by capitalizing on the following benefits:

  • Reduced cognitive load promotes better focus on both procedures and the object being studied
  • By overlaying AR instructions on objects, associations between the instructions and the object’s features and visual cues are more highly synchronized, thus promoting spatial learning
  • Collaborative features of AR can connect remote experts with trainees, providing instructor-led guidance

Generally, faster learning increases worker availability and encourages better problem solving. An additional benefit to enterprises is that they can use pilot project facilities and set ups for dual project assessment and training purposes.

Is your organization using or contemplating AR-assisted training programs?

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