Human Perception of Swarm Robot Motion

Dietz, Griffin; E, Jane L.; Washington, Peter; Kim, Lawrence H.; Follmer, Sean

doi:10.1145/3027063.3053220

Cited by 50 publications

(13 citation statements)

References 19 publications

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“…This was We are in this article reporting on results that approach significance, as it is done in many very recent publications, e.g. [65][66][67], which report on "trends" that are "not quite significant" or are "marginally significant".…”

Section: Changes In Scores and The Passage Of Timementioning

confidence: 98%

Kaspar in the wild: Experiences from deploying a small humanoid robot in a nursery school for children with autism

Syrdal

Dautenhahn

Robins

et al. 2020

Paladyn, Journal of Behavioral Robotics

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AbstractThis article describes a long-term study evaluating the use of the humanoid robot Kaspar in a specialist nursery for children with autism. The robot was used as a tool in the hands of teachers or volunteers, in the absence of the research team on-site. On average each child spent 16.53 months in the study. Staff and volunteers at the nursery were trained in using Kaspar and were using it in their day-to-day activities in the nursery. Our study combines an “in the wild” approach with a rigorous approach of collecting and including users’ feedback during an iterative evaluation and design cycle of the robot. This article focuses on the design of the study and the results from several interviews with the robot’s users. We also show results from the children’s developmental assessments by the teachers prior to and after the study. Results suggest a marked beneficial effect for the children from interacting with Kaspar. We highlight the challenges of transferring experimental technologies like Kaspar from a research setting into everyday practice in general and making it part of the day-to-day running of a nursery school in particular. Feedback from users led subsequently to many changes being made to Kaspar’s hardware and software. This type of invaluable feedback can only be gained in such long-term field studies.

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Section: Changes In Scores and The Passage Of Timementioning

confidence: 98%

Kaspar in the wild: Experiences from deploying a small humanoid robot in a nursery school for children with autism

Syrdal

Dautenhahn

Robins

et al. 2020

Paladyn, Journal of Behavioral Robotics

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“…Meanwhile, dispersion and stiffness allow to define the cohesiveness, synchronization and the general aspect of a formation of robots. Trajectory has already been studied in [10], while synchronization has been investigated in [8].…”

Section: Definitionmentioning

confidence: 99%

“…whereẋ i ∈ R n is the velocity of the i-th robot and B i ∈ R n×n , positive definite, is the damping factor (which should be introduced for a smooth movement, which has been found to be an important factor when designing an interaction task with mobile robots [8]). We consider a homogeneous friction coefficient along all directions, hence B i = bI.…”

Section: Robot Model and Independent Variablesmentioning

confidence: 99%

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Communication Through Motion: Legibility of Multi-Robot Systems

Capelli

Secchi

2019

2019 International Symposium on Multi-Robot and Multi-Agent Systems (MRS)

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The interaction between a user and a multi-robot system in a shared environment is a relatively uncharted topic. But, as these types of systems will increase in the future years, an efficient way of communication is necessary. To this aim, it is interesting to discover if a multi-robot system can communicate its intentions exploiting only some motion-variables, which are characteristics of the motion of the robots. This study is about the legibility of a multi-robot system: in particular, we focus on the influence of these motion-variables on the legibility of more than one group of robots that move in a shared environment with the user. These motion-variables are: trajectory, dispersion and stiffness. They are generally used to define the motion of a group of mobile robots. Trajectory and dispersion were found relevant for the correctness of the communication between the user and the multi-robot system, while stiffness was found relevant for the rapidity of communication. The analysis of the influence of the motion-variables was carried out with an ANOVA (analysis of variance) based on a series of data coming from an experimental campaign conducted in a virtual reality setup .

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“…In terms of existing platforms for swarm user interfaces, a notable contribution was the release of the tabletop robots named Zooids (Goc et al 2016). These robots led to a first study of robots group motion perception (emotional response) (Dietz et al 2017), and they were used to examine the perception of abstract robotic displays (Kim and Follmer 2017), an interesting approach for ubiquitous robots. The interactions in these studies are limited to physical manipulation of individual robots by the operator and passive observation of their behaviors.…”

Section: Human-swarm Interactionmentioning

confidence: 99%

Engaging with Robotic Swarms

St-Onge

Côté-Allard

Glette

et al. 2019

J. Hum.-Robot Interact.

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In recent years, researchers have explored human body posture and motion to control robots in more natural ways. These interfaces require the ability to track the body movements of the user in three dimensions. Deploying motion capture systems for tracking tends to be costly and intrusive and requires a clear line of sight, making them ill adapted for applications that need fast deployment. In this article, we use consumer-grade armbands, capturing orientation information and muscle activity, to interact with a robotic system through a state machine controlled by a body motion classifier. To compensate for the low quality of the information of these sensors, and to allow a wider range of dynamic control, our approach relies on machine learning. We train our classifier directly on the user to recognize (within minutes) which physiological state his or her body motion expresses. We demonstrate that on top of guaranteeing faster field deployment, our algorithm performs better than all comparable algorithms, and we detail its configuration and the most significant features extracted. As the use of large groups of robots is growing, we postulate that their interaction with humans can be eased by our approach. We identified the key factors to stimulate engagement using our system on 27 participants, each creating his or her own set of expressive motions to control a swarm of desk robots. The resulting unique dataset is available online together with the classifier and the robot control scripts.

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Human Perception of Swarm Robot Motion

Cited by 50 publications

References 19 publications

Kaspar in the wild: Experiences from deploying a small humanoid robot in a nursery school for children with autism

Kaspar in the wild: Experiences from deploying a small humanoid robot in a nursery school for children with autism

Communication Through Motion: Legibility of Multi-Robot Systems

Engaging with Robotic Swarms

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