Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey

Jiang, Shu; Arkin, Ronald C.

doi:10.21236/ada620347

Cited by 19 publications

(21 citation statements)

References 33 publications

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“…Additionally, in MI-HRI systems, which is the context of this work, initiative is mixed only when any agent (human or robot) is able to seize the initiative from the other [14,47]. It means that, in the MI context, allowing the operator to choose the control mode should only be possible if the interaction driving system considers it beneficial for the mission.…”

Section: Conclusion and Limitationsmentioning

confidence: 99%

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Chanel

Roy

Dehais

et al. 2020

Sensors

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The design of human–robot interactions is a key challenge to optimize operational performance. A promising approach is to consider mixed-initiative interactions in which the tasks and authority of each human and artificial agents are dynamically defined according to their current abilities. An important issue for the implementation of mixed-initiative systems is to monitor human performance to dynamically drive task allocation between human and artificial agents (i.e., robots). We, therefore, designed an experimental scenario involving missions whereby participants had to cooperate with a robot to fight fires while facing hazards. Two levels of robot automation (manual vs. autonomous) were randomly manipulated to assess their impact on the participants’ performance across missions. Cardiac activity, eye-tracking, and participants’ actions on the user interface were collected. The participants performed differently to an extent that we could identify high and low score mission groups that also exhibited different behavioral, cardiac and ocular patterns. More specifically, our findings indicated that the higher level of automation could be beneficial to low-scoring participants but detrimental to high-scoring ones, and vice versa. In addition, inter-subject single-trial classification results showed that the studied behavioral and physiological features were relevant to predict mission performance. The highest average balanced accuracy (74%) was reached using the features extracted from all input devices. These results suggest that an adaptive HRI driving system, that would aim at maximizing performance, would be capable of analyzing such physiological and behavior markers online to further change the level of automation when it is relevant for the mission purpose.

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Section: Conclusion and Limitationsmentioning

confidence: 99%

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Chanel

Roy

Dehais

et al. 2020

Sensors

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“…Research which focuses on investigating dynamic LOA switching on mobile robots is fairly limited. Furthermore, the investigation of MI systems to address this dynamic switching is even more limited, as highlighted by Jiang and Arkin [6] and Chiou et al [4]. A large part of the literature, e.g.…”

Section: Related Workmentioning

confidence: 99%

Experimental analysis of a variable autonomy framework for controlling a remotely operating mobile robot

Chiou

Bieksaite

Hawes

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In disaster recovery and search-and-rescue situations, for example, robots can be deployed in dangerous places while being teleoperated by an operator from a safe station. This distance can lead to some challenges for safe operation, such as communication delays and difficulties in monitoring and analyzing the situation [ 7 ]. Subsequently, a new research field emerged, which defined the rules for the autonomy levels that robots assume in specific tasks and in emergency situations or anomalous operations.…”

Section: Introductionmentioning

confidence: 99%

Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods

Palar

Terres

Oliveira

2020

Sensors

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This work discusses a novel human–robot interface for a climbing robot for inspecting weld beads in storage tanks in the petrochemical industry. The approach aims to adapt robot autonomy in terms of the operator’s experience, where a remote industrial joystick works in conjunction with an electromyographic armband as inputs. This armband is worn on the forearm and can detect gestures from the operator and rotation angles from the arm. Information from the industrial joystick and the armband are used to control the robot via a Fuzzy controller. The controller works with sliding autonomy (using as inputs data from the angular velocity of the industrial controller, electromyography reading, weld bead position in the storage tank, and rotation angles executed by the operator’s arm) to generate a system capable of recognition of the operator’s skill and correction of mistakes from the operator in operating time. The output from the Fuzzy controller is the level of autonomy to be used by the robot. The levels implemented are Manual (operator controls the angular and linear velocities of the robot); Shared (speeds are shared between the operator and the autonomous system); Supervisory (robot controls the angular velocity to stay in the weld bead, and the operator controls the linear velocity); Autonomous (the operator defines endpoint and the robot controls both linear and angular velocities). These autonomy levels, along with the proposed sliding autonomy, are then analyzed through robot experiments in a simulated environment, showing each of these modes’ purposes. The proposed approach is evaluated in virtual industrial scenarios through real distinct operators.

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Mixed-Initiative Human-Robot Interaction: Definition, Taxonomy, and Survey

Cited by 19 publications

References 33 publications

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Towards Mixed-Initiative Human–Robot Interaction: Assessment of Discriminative Physiological and Behavioral Features for Performance Prediction

Experimental analysis of a variable autonomy framework for controlling a remotely operating mobile robot

Human–Robot Interface for Embedding Sliding Adjustable Autonomy Methods

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