Short Time Delay Does Not Hinder Haptic Communication Benefits

Ivanova, Ekaterina; Eden, Jonathan; Zhu, Shenghuo; Carboni, Gerolamo; Yurkewich, Aaron; Burdet, Etienne

doi:10.1109/toh.2021.3079227

Cited by 11 publications

(10 citation statements)

References 18 publications

(32 reference statements)

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“…It has also been reported that humans do not perceive delays below 30 ms during continuous haptic interaction 8 . In a recent study, the effect of haptic delay for tasks relying on continuous haptic feedback is examined through a human–robot interaction paradigm 9 . Results demonstrated that subjects could recognize haptic delays starting from 20 ms, and at 60 ms and beyond, they clearly perceive the effect of the delay.…”

Section: Introductionmentioning

confidence: 99%

Midfrontal theta power encodes the value of haptic delay

Alsuradi

Park

Eid

2022

Sci Rep

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The use of haptic technologies in modern life scenarios is becoming the new normal particularly in rehabilitation, medical training, and entertainment applications. An evident challenge in haptic telepresence systems is the delay in haptic information. How humans perceive delayed visual and audio information has been extensively studied, however, the same for haptically delayed environments remains largely unknown. Here, we develop a visuo-haptic experimental setting that simulates pick and place task and involves continuous haptic feedback stimulation with four possible haptic delay levels. The setting is built using a haptic device and a computer screen. We use electroencephalography (EEG) to study the neural correlates that could be used to identify the amount of the experienced haptic delay. EEG data were collected from 34 participants. Results revealed that midfrontal theta oscillation plays a pivotal role in quantifying the amount of haptic delay while parietal alpha showed a significant modulation that encodes the presence of haptic delay. Based on the available literature, these results suggest that the amount of haptic delay is proportional to the neural activation that is associated with conflict detection and resolution as well as for multi-sensory divided attention.

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Section: Introductionmentioning

confidence: 99%

Midfrontal theta power encodes the value of haptic delay

Alsuradi

Park

Eid

2022

Sci Rep

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“…A: Participants tracked a randomly moving target with their wrist flexion/extension movement while being connected to a reactive robot partner (RP). B depicts the experimental protocol for the delay group [13] and noise group. The grey boxes represent the familiarisation/washout trials and the colourful blocks are the experimental conditions.…”

Section: Introductionmentioning

confidence: 99%

During haptic communication, the central nervous system compensates distinctly for delay and noise

Eden,

Ivanova,

Burdet

2024

Preprint

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Connected humans have been previously shown to exploit the exchange of haptic forces and tactile information to improve their performance in joint action tasks. As human interactions are increasingly mediated through robots and networks it is important to understand the impact that network features such as lag and noise may have on human behaviour. In this paper, we investigated the interaction with a human-like robot controller that provides similar haptic communication behaviour as human-human interaction and examined the influence and compensation mechanisms for delay and noise on haptic communication. The results of our experiments show that participants can distinguish between noise and delay, and make use of compensation mechanisms to preserve performance in both cases. However, while noise is compensated for by increasing co-contraction, delay compensation could not be explained by this strategy. Instead, computational modelling suggested that a feed-forward prediction mechanism is used to compensate for the temporal delay and yield an efficient haptic communication.

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“…Many interaction control methods tend to assign fixed roles of leader and follower to the human and the robot (such as in teleoperation [20]) or conversely to provide a fixed dynamic environment imposed on the user (such as in robot-assisted physical training [21]). However, while the human will automatically learn the robot control scheme and adapt to it [15], [22], the robot EJ Colgate). g: Humanrobot interaction for versatile operation in construction, showing two robots assisting in bolting the timber beams (reproduced with permission from Swiss NCCR Digital Fabrication / Roman Keller).…”

Section: Introductionmentioning

confidence: 99%

“…Many interaction control methods tend to assign fixed roles of leader and follower to the human and the robot (such as in teleoperation [20]) or conversely to provide a fixed dynamic environment imposed on the user (such as in robot-assisted physical training [21]). However, while the human will automatically learn the robot control scheme and adapt to it [15,22], the robot may also adapt its control to the human based on knowledge acquired through haptic communication. By understanding the human user control and using appropriate control theory or ML algorithms, the robot may develop appropriate interaction strategies to share effort considering their respective strengths [23,24].…”

Section: Introductionmentioning

confidence: 99%

A review on interaction control for contact robots through intent detection

Sena

Wang

et al. 2022

Prog. Biomed. Eng.

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Interaction control can take opportunities offered by contact robots physically interacting with their human user, such as assistance targeted to each human user, communication of goals to enable effective teamwork, and task-directed motion resistance in physical training and rehabilitation contexts. Here we review the burgeoning field of interaction control in the control theory and machine learning communities, by analysing the exchange of haptic information between the robot and its human user, and how they share the task effort. We first review the estimation and learning methods to predict the human user intent with the large uncertainty, variability and noise and limited observation of human motion. Based on this motion intent core, typical interaction control strategies are described using a homotopy of shared control parameters. Recent methods of haptic communication and game theory are then presented to consider the co-adaptation of human and robot control and yield versatile interactive control as observed between humans. Finally, the limitations of the presented state of the art are discussed and directions for future research are outlined.

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Short Time Delay Does Not Hinder Haptic Communication Benefits

Cited by 11 publications

References 18 publications

Midfrontal theta power encodes the value of haptic delay

Midfrontal theta power encodes the value of haptic delay

During haptic communication, the central nervous system compensates distinctly for delay and noise

A review on interaction control for contact robots through intent detection

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