Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

Iqbal, Tarıq; Rack, Samantha; Riek, Laurel D.

doi:10.1109/tro.2016.2570240

Cited by 74 publications

(36 citation statements)

References 46 publications

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“…Despite the incredible complexity of such human social interactions, we found that a rather simple mathematical model of coupled Kuramoto oscillators was able to capture most of the features observed experimentally. The availability of a mathematical description of the players' dynamics can be instrumental for designing better architectures driving virtual agents (e.g., robots, computer avatars) to coordinate their motion within groups of humans [53,54,55,56], as well as for predicting the coupling strength needed to restore synchronisation based on initial knowledge of individual consistency, group variance and topology.…”

Section: Discussionmentioning

confidence: 99%

Interaction patterns and individual dynamics shape the way we move in synchrony

Alderisio

Fiore

Salesse

et al. 2017

Sci Rep

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An important open problem in Human Behaviour is to understand how coordination emerges in human ensembles. This problem has been seldom studied quantitatively in the existing literature, in contrast to situations involving dual interaction. Here we study motor coordination (or synchronisation) in a group of individuals where participants are asked to visually coordinate an oscillatory hand motion. We separately tested two groups of seven participants. We observed that the coordination level of the ensemble depends on group homogeneity, as well as on the pattern of visual couplings (who looked at whom). Despite the complexity of social interactions, we show that networks of coupled heterogeneous oscillators with different structures capture well the group dynamics. Our findings are relevant to any activity requiring the coordination of several people, as in music, sport or at work, and can be extended to account for other perceptual forms of interaction such as sound or feel.

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Section: Discussionmentioning

confidence: 99%

Interaction patterns and individual dynamics shape the way we move in synchrony

Alderisio

Fiore

Salesse

et al. 2017

Sci Rep

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“…Hence, research groups now shifted to work on realising attention management, turn-taking gaze behaviour and other social gaze behaviour for robots in multi-party interac-tions (e.g., [12][13][14]). Motion in human groups has to be interpreted in real time to anticipate future actions of human group members and synthesize the robots' own motion accordingly [15].…”

Section: Technical Solutions To Handle Multiple Usersmentioning

confidence: 99%

I-C-E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

Abrams,

der Pütten

2019

Preprint

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The research community of human-robot interaction relies on theories and phenomena from the social sciences in order to study and validate robotic developments in interaction. These studies mainly concerned one (human) on one (robot) interactions in the past. The present paper shifts the attention to groups and group dynamics and reviews relevant concepts from the social sciences: in-group identification (I), cohesion (C) and entitativity (E). Ubiquitous robots will be part of larger social settings in the near future. A conceptual framework, the I-C-E framework, is proposed as a theoretical foundation for group (dynamics) research in HRI. Additionally, we present methods and possible measures for these relevant concepts and outline topics for future research.

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“…Imperfect information complicates the control design problem and may degrade the performance of the overall system. Cooperation with human was adopted to improve the performance of a multi-agent system in complex environments [9][10].To develop practical robot applications, it is necessary to take the environment into consideration, and the more complex the environment is, the more difficult it will be when designing the control law. Complex environments lead to complicated mechanisms, which makes the design problem complex.…”

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confidence: 99%

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New advances in complex motion control for single robot systems and multi-agent systems

Fang

Chen

2016

Sci. China Technol. Sci.

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Motion control is a classic problem, which has constantly attracted attentions from the control and the robotics communities. The problem used to be tackled in idealistic settings or using simplified models to complete complex tasks. Now the wish to control robots in realistic environments has driven the researchers to consider more and more complex systems and scenarios. This brings new challenges to the motion control problems.One that may complicate the motion control problem is the observability of the environment. In a fully observable environment, the observation of the robot can reveals the current state of the environment, which can then be used to design control laws [1][2][3]. In a partially observable environment, the robot may produce different observations for one state of the environment, because of incomplete information, also called imperfect information. The situation of imperfect information is often seen in multi-agent systems, where agent's communications are limited and interactions are complicated [4][5][6][7][8]. Imperfect information complicates the control design problem and may degrade the performance of the overall system. Cooperation with human was adopted to improve the performance of a multi-agent system in complex environments [9][10].To develop practical robot applications, it is necessary to take the environment into consideration, and the more complex the environment is, the more difficult it will be when designing the control law. Complex environments lead to complicated mechanisms, which makes the design problem complex. Recent studies focused on the controller design problem in complex environments for holonomic and nonholonomic robot systems, where different approaches to deal with the complexity were also proposed. Ref.[1] considered how to design a unified theoretical framework for both holonomic and nonholonomic robot systems in stairclimbing. A novel control law called active tension control combined with the computed torque method was presented for holonomic or nonholonomic robotic systems. There is also a corresponding work on realistic environment, such as rough terrains, where most of the existing bio-inspired legged robots do not possess walking abilities [2]. Generation of adaptive multiple gaits in rough terrains was considered and a central pattern generator (CPG)-based locomotion control method was proposed, integrated with a contact force feedback function. Another recently proposed method to solve the complex motion control problem for a single robot is to improve robot's structure combined with new controller design [3]. For example, ref.[3] introduced a ±50° yawing head joint that functions as the neck for improving turning ability, which enables the robotic fish to carry out complex motions. Correspondingly, an improved central pattern generator (CPG) model was also proposed.In multi-agent systems, communications and cooperations are unavoidable. This complicates the motion control problem. Usually, a simplified model leads to a simplified control system st...

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Movement Coordination in Human–Robot Teams: A Dynamical Systems Approach

Cited by 74 publications

References 46 publications

Interaction patterns and individual dynamics shape the way we move in synchrony

Interaction patterns and individual dynamics shape the way we move in synchrony

I-C-E Framework: Concepts for Group Dynamics Research in Human-Robot Interaction

New advances in complex motion control for single robot systems and multi-agent systems

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