Effects of Integrated Intent Recognition and Communication on Human-Robot Collaboration

Chang, Ming Che; Gutierrez, Reymundo A.; Khante, Priyanka; Short, Elaine Schaertl; Thomaz, Andrea L.

doi:10.1109/iros.2018.8593359

Cited by 17 publications

(9 citation statements)

References 12 publications

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“…However, examples of robot-of-human transparency, without using the term directly, can be found in Lorenz et al ( 2014 ); Lorenz ( 2015 ); Tsiourti and Weiss ( 2014 ); Dragan ( 2017 ); Wang et al ( 2017 ); Doellinger et al ( 2018 ); Goldhoorn et al ( 2018 ); Gui et al ( 2018 ), and Javdani et al ( 2018 ). In Casalino et al ( 2018 ) and Chang et al ( 2018 ) a feedback of the intent recognition is communicated to the operator.…”

Section: Transparency As Observability and Predictability Of The Smentioning

confidence: 99%

System Transparency in Shared Autonomy: A Mini Review

Alonso

Puente

2018

Front. Neurorobot.

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What does transparency mean in a shared autonomy framework? Different ways of understanding system transparency in human-robot interaction can be found in the state of the art. In one of the most common interpretations of the term, transparency is the observability and predictability of the system behavior, the understanding of what the system is doing, why, and what it will do next. Since the main methods to improve this kind of transparency are based on interface design and training, transparency is usually considered a property of such interfaces, while natural language explanations are a popular way to achieve transparent interfaces. Mechanical transparency is the robot capacity to follow human movements without human-perceptible resistive forces. Transparency improves system performance, helping reduce human errors, and builds trust in the system. One of the principles of user-centered design is to keep the user aware of the state of the system: a transparent design is a user-centered design. This article presents a review of the definitions and methods to improve transparency for applications with different interaction requirements and autonomy degrees, in order to clarify the role of transparency in shared autonomy, as well as to identify research gaps and potential future developments.

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Section: Transparency As Observability and Predictability Of The Smentioning

confidence: 99%

System Transparency in Shared Autonomy: A Mini Review

Alonso

Puente

2018

Front. Neurorobot.

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“…According to Lee et al [ 6 ], intent sensing is composed of three aspects: (i) the recognition and identification of activity transitions, (ii) the inference of task goals, and (iii) the prediction of future activities. See Figure 1 for an illustration of this.…”

Section: Introductionmentioning

confidence: 99%

Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks

Russell

Bergmann

Nagaraja

2022

Sensors

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Intent sensing—the ability to sense what a user wants to happen—has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.

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“…According to [8], intent sensing "includes recognizing the current activity, inferring the task goal, and predicting future actions." In this paper, the statement is refined slightly in that it refers only to positive activity leading to some change in the user's state.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Literature Review of Intent Sensing for Control of Medical Devices

Russell

Bergmann

2022

IEEE Trans. Med. Robot. Bionics

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The usefulness of medical devices, which require user input, is often limited by the control schemes that operate them. The recognition of user intent could enable far more intuitive control schemes that respond automatically to what the user wants the device to do. This paper provides a definition for intent, and then aims to systematically review current methods for sensing intent. It compares the accuracy of different methods and discusses how they might be combined. A systematic literature search was performed using IEEE Xplore, PubMed and Web of Science databases. 2311 papers were considered, reduced to 155 after review. All selected papers were assessed for quality using a checklist. The results identified and compared 15 sensing modalities used for intent sensing in a range of situations and applications that broadly fell into 12 distinct categories, with highly varying levels of accuracy. Several papers reached accuracy levels that could be suitable for everyday clinical application, but most work done on intent sensing to date has focused on activity transition classification, with fewer papers addressing task goal interference or predicting future actions. Further work can focus on the implementation of these kind of methods into a combined, context-aware intent-sensing control system.

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Effects of Integrated Intent Recognition and Communication on Human-Robot Collaboration

Cited by 17 publications

References 12 publications

System Transparency in Shared Autonomy: A Mini Review

System Transparency in Shared Autonomy: A Mini Review

Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks

A Systematic Literature Review of Intent Sensing for Control of Medical Devices

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