scholarly journals Toward Quantifying Trust Dynamics: How People Adjust Their Trust After Moment-to-Moment Interaction With Automation

2021 ◽  
pp. 001872082110347
Author(s):  
X. Jessie Yang ◽  
Christopher Schemanske ◽  
Christine Searle
Keyword(s):  
Recognition Task ◽  
Human Operator ◽  
Adjustment Process ◽  
Target Image ◽  
Adaptive Automation ◽  
Moment Interaction ◽  
Trust In Automation ◽  
Human Operators ◽  
Outcome Bias ◽  
Trial Participants

Objective We examine how human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Background Most existing studies measured trust by administering questionnaires at the end of an experiment. Only a limited number of studies viewed trust as a dynamic variable that can strengthen or decay over time. Method Seventy-five participants took part in an aided memory recognition task. In the task, participants viewed a series of images and later on performed 40 trials of the recognition task to identify a target image when it was presented with a distractor. In each trial, participants performed the initial recognition by themselves, received a recommendation from an automated decision aid, and performed the final recognition. After each trial, participants reported their trust on a visual analog scale. Results Outcome bias and contrast effect significantly influence human operators’ trust adjustments. An automation failure leads to a larger trust decrement if the final outcome is undesirable, and a marginally larger trust decrement if the human operator succeeds the task by him/herself. An automation success engenders a greater trust increment if the human operator fails the task. Additionally, automation failures have a larger effect on trust adjustment than automation successes. Conclusion Human operators adjust their trust in automation as a result of their moment-to-moment interaction with automation. Their trust adjustments are significantly influenced by decision-making heuristics/biases. Application Understanding the trust adjustment process enables accurate prediction of the operators’ moment-to-moment trust in automation and informs the design of trust-aware adaptive automation.

Brain Computer Interface Robotic Co-Workers: Defective Part Picking System

2018 ◽  
Author(s):  
Yao Li ◽  
Thenkurussi Kesavadas
Keyword(s):  
Direct Instruction ◽  
Human Subject ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Human Operator ◽  
Human Being ◽  
Unstructured Environment ◽  
Human Operators ◽  
Application Fields ◽  
Picking System

Industrial robotic co-workers are robots that can work with human being in an unstructured environment. Such robots, must be able to assist human operators in a seamless way without receiving specific instructions. Robotic co-workers can open entirely new application fields in manufacturing as demonstrated in this paper. We designed such an industrial co-robot to pick up defective parts by simply monitoring a human operator directly through a brain computer interface (BCI). By constantly monitoring the operator using BCI sensors, the robotic co-worker can sense when an operator notices a defective part and then moves to remove the part from a moving conveyor with no direct instruction from the operator. The robot, equipped with an RGB camera, recognizes the part, tracks the position and generates accurate motion plan. We demonstrated the system using a human subject study.

The Effects of Biodynamic Stress on Workload in Human Operators

1988 ◽  
Vol 32 (19) ◽  
pp. 1409-1413
Author(s):  
William B. Albery ◽  
Harry G. Armstrong ◽  
Merry M. Roe ◽  
Charles D. Goodyear ◽  
Kathy A. McCloskey
Keyword(s):  
Task Performance ◽  
Human Operator ◽  
Operator Performance ◽  
Human Operators ◽  
Operator Workload

The objective of this research was to assess the effects of two biodynamic stressors, noise and acceleration, commonly experienced in the aircraft cockpit, on human operator performance and workload. Thirteen workload measures, including one subjective, four performance and eight physiological, were recorded on subjects performing a dual psychomotor task. The results indicate that biodyanmic stressors such as noise and acceleration can adversely affect subjective operator workload without affecting objective task performance.

Nonlinear and Time-Varying Dynamical Models of Human Operators in Manual Control Systems

1966 ◽  
Vol 8 (2) ◽  
pp. 97-120 ◽  
Author(s):  
Walter W. Wierwille ◽  
Gilbert A. Gagne
Keyword(s):  
Control System ◽  
Linear Time ◽  
Human Operator ◽  
Manual Control ◽  
Time Variability ◽  
Time Varying ◽  
Deterministic Theory ◽  
Manual Control System ◽  
New Information ◽  
Human Operators

This paper describes the application of a deterministic theory for characterizing or modeling the dynamics of a human operator in a manual control system. Linear time-varying, nonlinear time-varying, and non-linear constant-coefficient models are obtained by applying the theory to tracking data taken for one- and two-axis tasks with various displays. The accuracy and fidelity of these advanced models are explored in detail. Also, new information about time variability and nonlinearity of the human operator, obtained by studying the models and the manual control system signals, is presented.

The Playbook™ Approach to Adaptive Automation

2005 ◽  
Vol 49 (1) ◽  
pp. 15-19 ◽  
Author(s):  
Christopher Miller ◽  
Harry Funk ◽  
Peggy Wu ◽  
Robert Goldman ◽  
John Meisner ◽  
...  
Keyword(s):  
Control System ◽  
Resource Usage ◽  
Shared Task ◽  
Adaptive Automation ◽  
Task Model ◽  
Integration Architecture ◽  
Human Operators ◽  
Ongoing Project

SIFT has pioneered a human-automation integration architecture, called Playbook™, based on a shared model of the tasks in the domain. This shared task model provides a means of human-automation communication about plans, goals, methods and resource usage—a process akin to referencing plays in a sports team's playbook. The Playbook enables human operators to interact with subordinate systems with the same flexibility as with well-trained human subordinates, thus allowing for adaptive automation. We describe this approach and its application in an ongoing project called Playbook-enhanced Variable Autonomy Control System™ (P-VACS).

Human Operator Performance Testing Using an Input-Shaped Bridge Crane

2005 ◽  
Vol 128 (4) ◽  
pp. 835-841 ◽  
Author(s):  
Attir Khalid ◽  
John Huey ◽  
William Singhose ◽  
Jason Lawrence ◽  
David Frakes
Keyword(s):  
Image Processing ◽  
Processing System ◽  
Performance Testing ◽  
Human Operator ◽  
Input Shaping ◽  
Bridge Crane ◽  
Georgia Institute ◽  
Operator Performance ◽  
Human Operators ◽  
Institute Of Technology

The payload oscillation inherent to all cranes makes it challenging for human operators to manipulate payloads quickly, accurately, and safely. An input-shaping controller was implemented on a large bridge crane at the Georgia Institute of Technology to reduce crane payload oscillation. The crane was used to study the performance of human operators as they drove the crane through obstacle courses. An image processing system was implemented to track the movement of the crane payload. Data from these experiments show that operators performed manipulation tasks faster, safer, and more effectively when input shaping was utilized to reduce payload sway.

Human Perception of the Maximum Safe Speed of Robot Motions

1987 ◽  
Vol 31 (2) ◽  
pp. 186-190 ◽  
Author(s):  
Waldemar Karwowski ◽  
T. Plank ◽  
M. Parsaei ◽  
M. Rahimi
Keyword(s):  
Selection Process ◽  
Human Perception ◽  
Industrial Robots ◽  
Adjustment Process ◽  
Maximum Speed ◽  
Initial Speed ◽  
Robot Arm ◽  
Human Operators ◽  
Arm Motion ◽  
Assembly Tasks

A laboratory experiment was conducted to determine the maximum speeds of robot arm motion considered by the subjects as safe for human operators working in a close proximity of the robot's working envelope. Twenty-nine college students (16 males and 13 females) participated in the study as monitors of the simulated assembly tasks performed by two industrial robots of different size and work capabilities. The results show that the speed selection process depends on the robot's physical size and its initial speed at the start of the adjustment process. Subjects selected higher speeds as “safe” if they were first exposed to the maximum speed of the robot, and significantly lower values when the initial speed of the robot's actions was only 5% of maximum. It was also shown that the subject's previous exposure to robots and the level of their knowledge of industrial robots highly affected their perception of safe speeds of robot motions. Such effects differ, however, between males and females.

Trends in HRC and their Effects on Human Operators’ Sense of Presence in Manufacturing Settings

2018 ◽  
Vol 62 (1) ◽  
pp. 1296-1300
Author(s):  
Himeka Hagiwara
Keyword(s):  
Human Operator ◽  
Sense Of Presence ◽  
Physical Presence ◽  
Human Operators ◽  
Manufacturing Environments

The introduction of robotic collaborators in manufacturing environments has the potential to interrupt human operators’ sense of physical presence when completing tasks, which can impact comfort, safety, and productivity. As a result, an examination of Human Robotic Collaboration (HRC) systems is called for in order to determine whether and how these systems are affecting human operator sense of presence. This review identifies trends and themes in HRC systems in order to identify patterns and opportunities for improvement.

Robotic Finishing of Interior Regions of Geometrically Complex Parts

2018 ◽  
Author(s):  
Ariyan M. Kabir ◽  
Aniruddha V. Shembekar ◽  
Rishi K. Malhan ◽  
Rohil S. Aggarwal ◽  
Joshua D. Langsfeld ◽  
...  
Keyword(s):  
Human Operator ◽  
Surface Finishing ◽  
Complex Geometries ◽  
Task Requirements ◽  
Planning Algorithm ◽  
Human Operators ◽  
High Level ◽  
Complex Parts ◽  
Programming Time ◽  
And Task

Surface finishing is an important manufacturing process. Many parts with complex geometries require finishing of internal regions before they can be used. In small and medium volume productions most of the finishing tasks are non-repetitive in nature, and have to be performed manually. These finishing operations for parts with complex geometries can be quite labor intensive, and may pose risk to humans. We have developed a collaborative finishing system where human operators work on high level decision making, and the robot assistants carry out the labor intensive low level finishing tasks. The human operator guides the robotic system by transferring operator knowledge through a user interface. Our system generates instructions for the robots based on the user inputs and task requirements. We have also developed a planning algorithm that automatically computes the paths for the robots by using the CAD model of the part. This significantly reduces the robot programming time and improves the efficiency of the finishing system. If needed, the system seeks help from the human operator by generating notifications.

Trusting Automation: Designing for Responsivity and Resilience

2021 ◽  
pp. 001872082110099
Author(s):  
Erin K. Chiou ◽  
John D. Lee
Keyword(s):  
Individual Characteristics ◽  
System Level ◽  
Organizational Contexts ◽  
Automation Systems ◽  
Trust In Automation ◽  
Human Operators ◽  
Narrative Literature Review ◽  
Relational Framework ◽  
Future Work ◽  
Research And Design

Objective This paper reviews recent articles related to human trust in automation to guide research and design for increasingly capable automation in complex work environments. Background Two recent trends—the development of increasingly capable automation and the flattening of organizational hierarchies—suggest a reframing of trust in automation is needed. Method Many publications related to human trust and human–automation interaction were integrated in this narrative literature review. Results Much research has focused on calibrating human trust to promote appropriate reliance on automation. This approach neglects relational aspects of increasingly capable automation and system-level outcomes, such as cooperation and resilience. To address these limitations, we adopt a relational framing of trust based on the decision situation, semiotics, interaction sequence, and strategy. This relational framework stresses that the goal is not to maximize trust, or to even calibrate trust, but to support a process of trusting through automation responsivity. Conclusion This framing clarifies why future work on trust in automation should consider not just individual characteristics and how automation influences people, but also how people can influence automation and how interdependent interactions affect trusting automation. In these new technological and organizational contexts that shift human operators to co-operators of automation, automation responsivity and the ability to resolve conflicting goals may be more relevant than reliability and reliance for advancing system design. Application A conceptual model comprising four concepts—situation, semiotics, strategy, and sequence—can guide future trust research and design for automation responsivity and more resilient human–automation systems.

scholarly journals Egocentric Gesture Recognition Using 3D Convolutional Neural Networks for the Spatiotemporal Adaptation of Collaborative Robots

2021 ◽  
Vol 15 ◽  
Author(s):  
Dimitris Papanagiotou ◽  
Gavriela Senteri ◽  
Sotiris Manitsaris
Keyword(s):  
Gesture Recognition ◽  
Pose Estimation ◽  
Cycle Time ◽  
Assembly Line ◽  
Vision System ◽  
Human Robot Interaction ◽  
Human Operator ◽  
Physical Interaction ◽  
Collaborative Robots ◽  
Human Operators

Collaborative robots are currently deployed in professional environments, in collaboration with professional human operators, helping to strike the right balance between mechanization and manual intervention in manufacturing processes required by Industry 4.0. In this paper, the contribution of gesture recognition and pose estimation to the smooth introduction of cobots into an industrial assembly line is described, with a view to performing actions in parallel with the human operators and enabling interaction between them. The proposed active vision system uses two RGB-D cameras that record different points of view of gestures and poses of the operator, to build an external perception layer for the robot that facilitates spatiotemporal adaptation, in accordance with the human's behavior. The use-case of this work is concerned with LCD TV assembly of an appliance manufacturer, comprising of two parts. The first part of the above-mentioned operation is assigned to a robot, strengthening the assembly line. The second part is assigned to a human operator. Gesture recognition, pose estimation, physical interaction, and sonic notification, create a multimodal human-robot interaction system. Five experiments are performed, to test if gesture recognition and pose estimation can reduce the cycle time and range of motion of the operator, respectively. Physical interaction is achieved using the force sensor of the cobot. Pose estimation through a skeleton-tracking algorithm provides the cobot with human pose information and makes it spatially adjustable. Sonic notification is added for the case of unexpected incidents. A real-time gesture recognition module is implemented through a Deep Learning architecture consisting of Convolutional layers, trained in an egocentric view and reducing the cycle time of the routine by almost 20%. This constitutes an added value in this work, as it affords the potential of recognizing gestures independently of the anthropometric characteristics and the background. Common metrics derived from the literature are used for the evaluation of the proposed system. The percentage of spatial adaptation of the cobot is proposed as a new KPI for a collaborative system and the opinion of the human operator is measured through a questionnaire that concerns the various affective states of the operator during the collaboration.

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