Fourth-person sensing for a service robot

Nakashima, Kouichi; Iwashita, Yumi; Pyo, Yoonseok; Takamine, Asamichi; Kurazume, Ryo

doi:10.1109/icsens.2015.7370461

Cited by 3 publications

(3 citation statements)

References 10 publications

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“…Robots computation facilities are offered as a cloud service to the end users in [129]. Moreover, the on-board sensors of the robots offer sensing-as-a-Service by perceiving information from the environment and support the processing and actuation, based on the sensed data [178]. Sensing capability of the robots is provided as a service in [179] for door-opening control problem in the real environment.…”

Section: ) Raasmentioning

confidence: 99%

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

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Robotic applications nowadays are widely adopted to enhance operational automation and performance of real-world Cyber-Physical Systems (CPSs) including Industry 4.0, agriculture, healthcare, and disaster management. These applications are composed of latency-sensitive, data-heavy, and compute-intensive tasks. The robots, however, are constrained in the computational power and storage capacity. The concept of multi-agent cloud robotics enables robotto-robot cooperation and creates a complementary environment for the robots in executing large-scale applications with the capability to utilize the edge and cloud resources. However, in such a collaborative environment, the optimal resource allocation for robotic tasks is challenging to achieve. Heterogeneous energy consumption rates and application of execution costs associated with the robots and computing instances make it even more complex. In addition, the data transmission delay between local robots, edge nodes, and cloud data centres adversely affects the real-time interactions and impedes service performance guarantee. Taking all these issues into account, this paper comprehensively surveys the state-of-the-art on resource allocation and service provisioning in multi-agent cloud robotics. The paper presents the application domains of multi-agent cloud robotics through explicit comparison with the contemporary computing paradigms and identifies the specific research challenges. A complete taxonomy on resource allocation is presented for the first time, together with the discussion of resource pooling, computation offloading, and task scheduling for efficient service provisioning. Furthermore, we highlight the research gaps from the learned lessons, and present future directions deemed beneficial to further advance this emerging field.

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Section: ) Raasmentioning

confidence: 99%

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Afrin

Jin

Rahman

et al. 2021

IEEE Commun. Surv. Tutorials

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“…Notably, we refer the concept as to "fourth-person vision". The perspective term "fourth-person" was initially introduced into our previous study [12], which is an analogy of storytellers or readers of books. As illustrated in Fig.…”

Section: Fourth-person Visionmentioning

confidence: 99%

“…In this paper, we introduce the novel lifelogging concept "fourth-person vision", which complementary exploits the first-, second-, and third-person images as described above to generate accurate and detailed descriptions. The perspective term "fourth-person" was initially introduced into our previous study [12], which is an analogy of a storyteller or a book reader who picks up unique information within multi-perspective sentences and appreciates the storylines, as illustrated in Fig. 1.…”

Section: Introductionmentioning

confidence: 99%

Lifelogging caption generation via fourth-person vision in a human–robot symbiotic environment

2020

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Automatic analysis of our daily lives and activities through a first-person lifelog camera provides us with opportunities to improve our life rhythms or to support our limited visual memories. Notably, to express the visual experiences, the task of generating captions from first-person lifelog images has been actively studied in recent years. First-person images involve scenes approximating what users actually see; therein, the visual cues are not enough to express the user’s context since the images are limited by his/her intention. Our challenge is to generate lifelog captions using a meta-perspective called “fourth-person vision”. The “fourth-person vision” is a novel concept which complementary exploits the visual information from the first-, second-, and third-person perspectives. First, we assume human–robot symbiotic scenarios that provide a second-person perspective from the camera mounted on the robot and a third-person perspective from the camera fixed in the symbiotic room. To validate our approach in this scenario, we collect perspective-aware lifelog videos and corresponding caption annotations. Subsequently, we propose a multi-perspective image captioning model composed of an image-wise salient region encoder, an attention module that adaptively fuses the salient regions, and a caption decoder that generates scene descriptions. We demonstrate that our proposed model based on the fourth-person concept can greatly improve the captioning performance against single- and double-perspective models.

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Fourth-Person Captioning: Describing Daily Events by Uni-supervised and Tri-regularized Training

Nakashima

Iwashita

Kawamura³

et al. 2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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Fourth-person sensing for a service robot

Cited by 3 publications

References 10 publications

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Resource Allocation and Service Provisioning in Multi-Agent Cloud Robotics: A Comprehensive Survey

Lifelogging caption generation via fourth-person vision in a human–robot symbiotic environment

Fourth-Person Captioning: Describing Daily Events by Uni-supervised and Tri-regularized Training

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