The Shared Control Paradigm for Assistive and Rehabilitation Robots

2015 ◽  
pp. 613-616
Author(s):  
Cristina Urdiales García
Keyword(s):  
Shared Control ◽  
Rehabilitation Robots ◽  
Control Paradigm

Towards Explainable Co-Robots: Developing Confidence-Based Shared Control Paradigms

2020 ◽  
Author(s):  
Daniel Saraphis ◽  
Vahid Izadi ◽  
Amirhossein Ghasemi
Keyword(s):  
Numerical Simulations ◽  
Shared Control ◽  
Automated System ◽  
Generic Model ◽  
Automation System ◽  
Steering Control ◽  
Control Paradigm ◽  
Control Framework ◽  
Automation Systems ◽  
Control Authority

Abstract In this paper, we aim to develop a shared control framework wherein the control authority is dynamically allocated between the human operator and the automation system. To this end, we have defined a shared control paradigm wherein the blending mechanism uses the confidence between a human and co-robot to allocate the control authority. To capture the confidence between the human and robot, qualitatively, a simple-but-generic model is presented wherein the confidence of human-to-robot and robot-to-human is a function of the human’s performance and robot’s performance. The computed confidence will then be used to adjust the level of autonomy between the two agents dynamically. To validate our novel framework, we propose case studies in which the steering control of a semi-automated system is shared between the human and onboard automation systems. The numerical simulations demonstrate the effectiveness of the proposed shared control paradigms.

scholarly journals Task-based hybrid shared control for training through forceful interaction

2020 ◽  
Vol 39 (9) ◽  
pp. 1138-1154
Author(s):  
Kathleen Fitzsimons ◽  
Aleksandra Kalinowska ◽  
Julius P Dewald ◽  
Todd D Murphey
Keyword(s):  
Skill Acquisition ◽  
Hybrid Approach ◽  
Time Instant ◽  
Human Robot Interaction ◽  
Evaluation Criterion ◽  
Shared Control ◽  
Control Paradigm ◽  
Specific Feedback ◽  
Error Amplification ◽  
User Actions

Despite the fact that robotic platforms can provide both consistent practice and objective assessments of users over the course of their training, there are relatively few instances where physical human–robot interaction has been significantly more effective than unassisted practice or human-mediated training. This article describes a hybrid shared control robot, which enhances task learning through kinesthetic feedback. The assistance assesses user actions using a task-specific evaluation criterion and selectively accepts or rejects them at each time instant. Through two human subject studies (total [Formula: see text]), we show that this hybrid approach of switching between full transparency and full rejection of user inputs leads to increased skill acquisition and short-term retention compared with unassisted practice. Moreover, we show that the shared control paradigm exhibits features previously shown to promote successful training. It avoids user passivity by only rejecting user actions and allowing failure at the task. It improves performance during assistance, providing meaningful task-specific feedback. It is sensitive to initial skill of the user and behaves as an “assist-as-needed” control scheme, adapting its engagement in real time based on the performance and needs of the user. Unlike other successful algorithms, it does not require explicit modulation of the level of impedance or error amplification during training and it is permissive to a range of strategies because of its evaluation criterion. We demonstrate that the proposed hybrid shared control paradigm with a task-based minimal intervention criterion significantly enhances task-specific training.

scholarly journals Transparent Intent for Explainable Shared Control in Assistive Robotics

2020 ◽  
Author(s):  
Mark Zolotas ◽  
Yiannis Demiris
Keyword(s):  
Mental Models ◽  
Research Problem ◽  
Assistive Robotics ◽  
Shared Control ◽  
Control Paradigm ◽  
Human Robot Collaboration

Robots supplied with the ability to infer human intent have many applications in assistive robotics. In these applications, robots rely on accurate models of human intent to administer appropriate assistance. However, the effectiveness of this assistance also heavily depends on whether the human can form accurate mental models of robot behaviour. The research problem is to therefore establish a transparent interaction, such that both the robot and human understand each other’s underlying "intent". We situate this problem in our Explainable Shared Control paradigm and present ongoing efforts to achieve transparency in human-robot collaboration.

scholarly journals VFH+ based shared control for remotely operated mobile robots

2020 ◽  
Author(s):  
Pantelis Pappas ◽  
Manolis Chiou ◽  
Georgios-Theofanis Epsimos ◽  
Grigoris Nikolaou ◽  
Rustam Stolkin
Keyword(s):  
Mobile Robots ◽  
Shared Control

scholarly journals A Shared-Control Teleoperation Architecture for Nonprehensile Object Transportation

2021 ◽  
pp. 1-15
Author(s):  
Mario Selvaggio ◽  
Jonathan Cacace ◽  
Claudio Pacchierotti ◽  
Fabio Ruggiero ◽  
Paolo Robuffo Giordano
Keyword(s):  
Shared Control ◽  
Object Transportation
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