Control characteristics of two humans in cooperative task and its application to robot control

Rahman, Mozasser; Ikeura, Ryojun; Mizutani, Kazuki

doi:10.1109/iecon.2000.972544

Cited by 21 publications

(22 citation statements)

References 8 publications

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“…Even though HHI has been studied by many researchers to transfer physical collaboration skills to human-robot teams [4], [5], [6], [7], [8], [9], not many studies inform the decision process based on an understanding of human-human interaction (HHI). In fact, there are only a few studies that made an effort to define interaction patterns for pHHI.…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Küçükyılmaz

Issak

2020

IEEE Robot. Autom. Lett.

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Joint object transfer is a complex task, which is less structured and less specific than what is existing in several industrial settings. When two humans are involved in such a task, they cooperate through different modalities to understand the interaction states during operation and mutually adapt to one another's actions. Mutual adaptation implies that both partners can identify how well they collaborate (i.e. infer about the interaction state) and act accordingly. These interaction states can define whether the partners work in harmony, face conflicts, or remain passive during interaction. Understanding how two humans work together during physical interactions is important when exploring the ways a robotic assistant should operate under similar settings. This study acts as a first step to implement an automatic classification mechanism during ongoing collaboration to identify the interaction state during object comanipulation. The classification is done on a dataset consisting of data from 40 subjects, who are partnered to form 20 dyads. The dyads experiment in a physical human-human interaction (pHHI) scenario to move an object in an haptics-enabled virtual environment to reach predefined goal configurations. In this study, we propose a sliding-window approach for feature extraction and demonstrate the online classification methodology to identify interaction patterns. We evaluate our approach using 1) a support vector machine classifier (SVMc) and 2) a Gaussian Process classifier (GPc) for multi-class classification, and achieve over 80% accuracy with both classifiers when identifying general interaction types.

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Section: Introductionmentioning

confidence: 99%

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Küçükyılmaz

Issak

2020

IEEE Robot. Autom. Lett.

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“…The experimental results demonstrated that the time taken to achieve the targets by two humans working together is faster than the average time of a human working individually. The human arm impedance model [8,9] Raman et al [8 and 9] investigated control characteristics of two humans working on a cooperative task, and have designed control systems for cooperative robots to work with other partners by imitating human based behaviour strategy. Before being implemented on a robot to mimic the same interactive tasks with a human partner, the human's arm impedances were first analysed based on the arm's dynamic model, which can be mechanically represented in to a spring dashpot-and-mass system, as shown in Fig.…”

Section: A Backgroundmentioning

confidence: 99%

“…The receiver force (!r) is always zero because particularly the sending phase is proposed. In state space design, it specifically requires the system characteristic equations as defined in (8)(9)(10)(11).…”

Section: Machine Transfer Fucntionmentioning

confidence: 99%

Human-human interaction using a behavioural control strategy

Neranon

Bicker

2013

2013 IEEE Conference on Technologies for Practical Robot Applications (TePRA)

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This paper presents an outline of human-human interaction to establish a framework to understand how a behaviour based approach can be developed in the design of a human-robot interactive strategy. To approach the conceptual design guidelines for an interactive human-robot strategy, the mathematical model of human behaviour during transferring the compliant object to a receiver without any types of communication has been strategically analysed. The AutoRegressive Moving Average with Exogenous Input (ARMAX) system identification has been applied to identify the human arm model. A set of experiments have been designed (based on BoxBehnken), along with the influence variables affecting the human forces, which consist of mass, friction and target displacement.The estimated ARMAX models were shown to be good matching with the actual experimental data, where the best-fit percentages of human force profiles are between 88.73% -97.2%; the proposed models can then be used to present the human arm characteristics effectively.

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“…They only had visual and haptic information available to them. In many studies either the subjects are not permitted to speak at all [11,19] or verbal communication is not consistently controlled or recorded [20,21]. There has been work done by Huang et al [22] that specifically focuses on auditory (not verbal) cues.…”

Section: Verbal Communication In Human-human Teamsmentioning

confidence: 99%

“…The model has been studied in the context of a manipulation task [24], and it was found that humans naturally prefer to work in a team where one partner is slightly more dominant. Similar models have been implemented in shared control architectures as well [21,25,26].…”

Section: Leader-follower Architectures For Cooperative Manipulationmentioning

confidence: 99%

The Role of Verbal and Nonverbal Communication in a Two-Person, Cooperative Manipulation Task

Parikh

Esposito

Searock

2014

Advances in Human-Computer Interaction

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Motivated by the differences between human and robot teams, we investigated the role of verbal communication between human teammates as they work together to move a large object to a series of target locations. Only one member of the group was told the target sequence by the experimenters, while the second teammate had no target knowledge. The two experimental conditions we compared were haptic-verbal (teammates are allowed to talk) and haptic only (no talking allowed). The team’s trajectory was recorded and evaluated. In addition, participants completed a NASA TLX-style postexperimental survey which gauges workload along 6 different dimensions. In our initial experiment we found no significant difference in performance when verbal communication was added. In a follow-up experiment, using a different manipulation task, we did find that the addition of verbal communication significantly improved performance and reduced the perceived workload. In both experiments, for the haptic-only condition, we found that a remarkable number of groups independently improvised common haptic communication protocols (CHIPs). We speculate that such protocols can be substituted for verbal communication and that the performance difference between verbal and nonverbal communication may be related to how easy it is to distinguish the CHIPs from motions required for task completion.

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Control characteristics of two humans in cooperative task and its application to robot control

Cited by 21 publications

References 8 publications

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Online Identification of Interaction Behaviors From Haptic Data During Collaborative Object Transfer

Human-human interaction using a behavioural control strategy

The Role of Verbal and Nonverbal Communication in a Two-Person, Cooperative Manipulation Task

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