Review of Interfaces for Industrial Human-Robot Interaction

Berg, Julia; Lu, Shuang

doi:10.1007/s43154-020-00005-6

Cited by 71 publications

(27 citation statements)

References 47 publications

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“…Existing work has highlighted the importance of the interface when a human and robot collectively share the same workspace, with key considerations being ease of use, specificity of communication, and reliability of feedback [33], [34]. Current methods suggest using visual displays, hand or face gestures, physical interaction and haptics, and verbal language can all be viable solutions towards effective human communication [35]. However, less work has been done in interfaces for how the robot can effectively communicate the representation of what it has learned with the human.…”

Section: Learning Human-guided Representationsmentioning

confidence: 99%

Aligning Robot Representations with Humans

Bobu¹,

Andi²

2022

Preprint

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As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging. A central challenge remains that often, it is difficult (perhaps even impossible) to capture the full complexity of the deployment environment, and therefore the desired tasks, at training time. Consequently, the representation, or abstraction, of the tasks the human hopes for the robot to perform in one environment may be misaligned with the representation of the tasks that the robot has learned in another. We postulate that because humans will be the ultimate evaluator of system success in the world, they are best suited to communicating the aspects of the tasks that matter to the robot. Our key insight is that effective learning from human input requires first explicitly learning good intermediate representations and then using those representations for solving downstream tasks. We highlight three areas where we can use this approach to build interactive systems and offer future directions of work to better create advanced collaborative robots.

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Section: Learning Human-guided Representationsmentioning

confidence: 99%

Aligning Robot Representations with Humans

Bobu¹,

Andi²

2022

Preprint

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“…When it comes to establishing the interplay between humans and robots, far from relying on a direct manipulation, CPS bring a wide range of mediating user interfaces that pursue a more natural interaction according to various user-interface requirements [29]. To date, gesture recognition, speech recognition and multimodal approaches are of particular interest [30]. Gesture recognition is regarded as particularly intuitive and effective, which is typically addressed using mounted cameras-visionbased approaches-or embedded body sensors-wearable approaches.…”

Section: Gesture Recognition For Hrimentioning

confidence: 99%

Comparison of RGB-D and IMU-based gesture recognition for human-robot interaction in remanufacturing

Roda-Sánchez

Garrido-Hidalgo

García

et al. 2021

Int J Adv Manuf Technol

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With product life-cycles getting shorter and limited availability of natural resources, the paradigm shift towards the circular economy is being impulsed. In this domain, the successful adoption of remanufacturing is key. However, its associated process efficiency is to date limited given high flexibility requirements for product disassembly. With the emergence of Industry 4.0, natural human-robot interaction is expected to provide numerous benefits in terms of (re)manufacturing efficiency and cost. In this regard, vision-based and wearable-based approaches are the most extended when it comes to establishing a gesture-based interaction interface. In this work, an experimental comparison of two different movement-estimation systems—(i) position data collected from Microsoft Kinect RGB-D cameras and (ii) acceleration data collected from inertial measurement units (IMUs)—is addressed. The results point to our IMU-based proposal, OperaBLE, having recognition accuracy rates up to 8.5 times higher than these of Microsoft Kinect, which proved to be dependent on the movement’s execution plane, subject’s posture, and focal distance.

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“…The boundaries between the operator and machines are becoming less evident and tangible while the technologies involved in the modern production processes have a direct impact on the human workers, as they support and perfect human skills by sensing and perceptive technologies throughout the production lifecycle [2]. The newly born teaming paradigms are in need of natural, adaptive and intuitive Human Machine Interfaces (HMIs) that could support safety in HRC but also promote wellbeing and technology accep tance from the human operator perspective [3]. Recent studies show how Digital Twins (DTs) AR/VR interfaces can be valuable tools in the evaluation of safety [4] programming methods [5] and commissioning of HRC systems, while only a few studies attempt the combination of DT AR/VR technologies in a User-Centred Design (UCD) approach focusing on specific use cases.…”

Section: Introductionmentioning

confidence: 99%