Affective feedback in closed loop human-robot interaction

2006 ◽  
Author(s):  
Pramila Rani ◽  
Changchun Liu ◽  
Nilanjan Sarkar
Keyword(s):  
Closed Loop ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Affective Feedback

An Approach to Telerobotic Manipulations

1997 ◽  
Vol 119 (3) ◽  
pp. 431-438 ◽  
Author(s):  
H. Kazerooni ◽  
C. L. Moore
Keyword(s):  
Closed Loop ◽  
Control Method ◽  
Sufficient Conditions ◽  
Human Robot Interaction ◽  
Loop System ◽  
Robotic Manipulation ◽  
Closed Loop System ◽  
Robot Interaction ◽  
Minimum Number ◽  
The Stability

This article introduces three areas of study: 1 telefunctioning; 2 a control method for producing telefunctioning; and 3 an analysis of human-robot interaction when telefunctioning governs the system behavior. Telefunctioning facilitates the maneuvering of loads by creating a perpetual sense of the load dynamics for the operator. Telefunctioning is defined as a robotic manipulation method in which the dynamic behaviors of the slave robot and the master robot are functions of each other; these functions are the designer’s choice and depend on the application. (In a subclass of telefunctioning currently referred to as telepresence, these functions are specified as “unity” so that the master and slave variables (e.g., position, velocity) are dynamically equal.) To produce telefunctioning, this work determines a minimum number of functions relating the robots’ variables, and then develops a control architecture which guarantees that the defined functions govern the dynamic behavior of the closed-loop system. The stability of the closed-loop system (i.e., master robot, slave robot, human, and the load being manipulated) is analyzed and sufficient conditions for stability are derived.

scholarly journals Reinforcement learning for human-robot shared control

2019 ◽  
Vol 40 (1) ◽  
pp. 105-117
Author(s):  
Yanan Li ◽  
Keng Peng Tee ◽  
Rui Yan ◽  
Shuzhi Sam Ge
Keyword(s):  
Reinforcement Learning ◽  
Closed Loop ◽  
Human Robot Interaction ◽  
Shared Control ◽  
Closed Loop System ◽  
Robot System ◽  
Robot Interaction ◽  
Learning Capability ◽  
Content Type ◽  
Control Objective

Purpose This paper aims to propose a general framework of shared control for human–robot interaction. Design/methodology/approach Human dynamics are considered in analysis of the coupled human–robot system. Motion intentions of both human and robot are taken into account in the control objective of the robot. Reinforcement learning is developed to achieve the control objective subject to unknown dynamics of human and robot. The closed-loop system performance is discussed through a rigorous proof. Findings Simulations are conducted to demonstrate the learning capability of the proposed method and its feasibility in handling various situations. Originality/value Compared to existing works, the proposed framework combines motion intentions of both human and robot in a human–robot shared control system, without the requirement of the knowledge of human’s and robot’s dynamics.

scholarly journals FES Cycling and Closed-Loop Feedback Control for Rehabilitative Human–Robot Interaction

Robotics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 61
Author(s):  
Christian Cousin ◽  
Victor Duenas ◽  
Warren Dixon
Keyword(s):  
Closed Loop ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Adaptive Controllers ◽  
Rehabilitation Outcomes ◽  
Rehabilitation Robots ◽  
Trade Offs ◽  
Loop Feedback ◽  
Neurological Injuries ◽  
Control Designs

For individuals with movement impairments due to neurological injuries, rehabilitative therapies such as functional electrical stimulation (FES) and rehabilitation robots hold vast potential to improve their mobility and activities of daily living. Combining FES with rehabilitation robots results in intimately coordinated human–robot interaction. An example of such interaction is FES cycling, where motorized assistance can provide high-intensity and repetitive practice of coordinated limb motion, resulting in physiological and functional benefits. In this paper, the development of multiple FES cycling testbeds and safeguards is described, along with the switched nonlinear dynamics of the cycle–rider system. Closed-loop FES cycling control designs are described for cadence and torque tracking. For each tracking objective, the authors’ past work on robust and adaptive controllers used to compute muscle stimulation and motor current inputs is presented and discussed. Experimental results involving both able-bodied individuals and participants with neurological injuries are provided for each combination of controller and tracking objective. Trade-offs for the control algorithms are discussed based on the requirements for implementation, desired rehabilitation outcomes and resulting rider performance. Lastly, future works and the applicability of the developed methods to additional technologies including teleoperated robotics are outlined.

Workload in Human-Robot Interaction: A Review of Manipulations and Outcomes

2009 ◽  
Author(s):  
Matthew S. Prewett ◽  
Kristin N. Saboe ◽  
Ryan C. Johnson ◽  
Michael D. Coovert ◽  
Linda R. Elliott
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction

Biomimetic, sociable robots for human-robot interaction

2010 ◽  
Author(s):  
Eleanore Edson ◽  
Judith Lytle ◽  
Thomas McKenna
Keyword(s):  
Human Robot Interaction ◽  
Robot Interaction

The role and relationship of mindreading and social attunement in HRI – position statements of interdisciplinary researchers. Workshop HRI'20

2020 ◽  
Author(s):  
Agnieszka Wykowska ◽  
Jairo Pérez-Osorio ◽  
Stefan Kopp
Keyword(s):  
Mental States ◽  
Human Robot Interaction ◽  
Human Interaction ◽  
Artificial Agents ◽  
The Novel ◽  
Robot Interaction ◽  
Novel Coronavirus ◽  
Relationship Of ◽  
The Relationship

This booklet is a collection of the position statements accepted for the HRI’20 conference workshop “Social Cognition for HRI: Exploring the relationship between mindreading and social attunement in human-robot interaction” (Wykowska, Perez-Osorio & Kopp, 2020). Unfortunately, due to the rapid unfolding of the novel coronavirus at the beginning of the present year, the conference and consequently our workshop, were canceled. On the light of these events, we decided to put together the positions statements accepted for the workshop. The contributions collected in these pages highlight the role of attribution of mental states to artificial agents in human-robot interaction, and precisely the quality and presence of social attunement mechanisms that are known to make human interaction smooth, efficient, and robust. These papers also accentuate the importance of the multidisciplinary approach to advance the understanding of the factors and the consequences of social interactions with artificial agents.

Shall I trust you? From child human-robot interaction to trusting relationships

2019 ◽  
Author(s):  
Cinzia Di Dio ◽  
Federico Manzi ◽  
Giulia Peretti ◽  
Angelo Cangelosi ◽  
Paul L. Harris ◽  
...  
Keyword(s):  
Behavioral Pattern ◽  
Human Robot Interaction ◽  
School Age Children ◽  
Social Relevance ◽  
Robot Interaction ◽  
Cognitive Correlates ◽  
The Social ◽  
The Relationship

Studying trust within human-robot interaction is of great importance given the social relevance of robotic agents in a variety of contexts. We investigated the acquisition, loss and restoration of trust when preschool and school-age children played with either a human or a humanoid robot in-vivo. The relationship between trust and the quality of attachment relationships, Theory of Mind, and executive function skills was also investigated. No differences were found in children’s trust in the play-partner as a function of agency (human or robot). Nevertheless, 3-years-olds showed a trend toward trusting the human more than the robot, while 7-years-olds displayed the reverse behavioral pattern, thus highlighting the developing interplay between affective and cognitive correlates of trust.

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