ARMEN: Assistive robotics to maintain elderly people in natural environment

Leroux, Christophe; Lebec, Olivier; Ghezala, Mohamed Walid Ben; Mezouar, Youcef; Devillers, Laurence; Chastagnol, Clément; Martin, Jocelyn; Leynaert, Violaine; Fattal, Charles

doi:10.1016/j.irbm.2013.01.012

Cited by 17 publications

(14 citation statements)

References 2 publications

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“…In other words, the perceptual knowledge of these robots is static and they are unable to adapt to dynamic environments. Examples of such service robots that have demonstrated perception and action coupling include ARMEN [111], El-E [73], Busboy [178], TUM Rosie [11], TORO [35], WALK-MAN [189], ARMAR [10], DLR's Rollin'Justin [45], Automower [3], Spot [1], Fetch [203] and TIAGo [149]. The characteristics of these robots are summarized in Table 1.…”

Section: Assistive and Service Robotsmentioning

confidence: 99%

“…In the ARMEN project, Leroux et al [111] proposed a mobile assistive robotics approach providing advanced functions to help care for elderly or disabled people at home. This project mainly involves object manipulation, knowledge representation, and object recognition.…”

Section: Assistive and Service Robotsmentioning

confidence: 99%

“…El-E [73] -3D vision based on a stereocamera, a monochrome camera and a laser scanner -One arm manipulator -Two fingered gripper with force sensors -Wheels that allow for mobility -Can be directed by a human using a laster pointer ARMEN [111] -2D vision based on a greyscale camera -One arm manipulator -Three fingered gripper -Wheels that allow for mobility -Visual memory building -Interaction with humans via a virtual conversational agent Busboy [178] -Consists of two cooperating robots -2D vision based on external camera -One arm manipulator on one of the robots -Three fingered grasper -Both are mobile wheeled robots TUM Rosie and James [11] -Consists of two cooperating robots -3D vision based on a combination of a tilting laser and a ToF camera -Both robots have two manipulators -One has two fingered grippers while the other has 4 fingered human-like grippers -Both are mobile wheeled robots -Able to learn new tasks from instructions on the internet TORO [35] -3D vision based on a stereo camera and depth sensor -Two manipulators -Two anthropomorphic hands -Bipedal motion WALK-MAN [189] -3D vision based on a stereo camera and a laser sensor -Two manipulators -Two anthropomorphic hands -Bipedal motion ARMAR [10] -3D vision based on a stereo camera, a laser sensor and a RGB-D sensor -Two manipulators -Two anthropomorphic hands -Wheels that allow for mobility -Human robot interaction via pose estimation, verbal and physical interactions DLR's Rollin'Justin [45] -3D vision photo mixer device cameras -Two manipulators -Two anthropomorphic hands -Wheels that allow for mobility Fetch [203] -3D vision based on RGB-D cameras -One arm manipulator -Two fingered gripper -Wheels that allow for mobility TIAGo [149] -3D vision based on RGB-D cameras -One arm manipulator (two arms are also possible) -Anthropomorphic hands -Wheels that allow for mobility -Several modular options Nevertheless, more advanced and versatile commercial service robots have also been developed. Boston Dynamics' Spot [1] is an example of a highly mobile quadrupedal robot.…”

Section: Robots Characteristicsmentioning

confidence: 99%

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The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

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Service robots are appearing more and more in our daily life. The development of service robots combines multiple fields of research, from object perception to object manipulation. The state-of-the-art continues to improve to make a proper coupling between object perception and manipulation. This coupling is necessary for service robots not only to perform various tasks in a reasonable amount of time but also to continually adapt to new environments and safely interact with non-expert human users. Nowadays, robots are able to recognize various objects, and quickly plan a collision-free trajectory to grasp a target object in predefined settings. Besides, in most of the cases, there is a reliance on large amounts of training data. Therefore, the knowledge of such robots is fixed after the training phase, and any changes in the environment require complicated, time-consuming, and expensive robot re-programming by human experts. Therefore, these approaches are still too rigid for real-life applications in unstructured environments, where a significant portion of the environment is unknown and cannot be directly sensed or controlled. In such environments, no matter how extensive the training data used for batch learning, a robot will always face new objects. Therefore, apart from batch learning, the robot should be able to continually learn about new object categories and grasp affordances from very few training examples on-site. Moreover, apart from robot self-learning, non-expert users could interactively guide the process of experience acquisition by teaching new concepts, or by correcting insufficient or erroneous concepts. In this way, the robot will constantly learn how to help humans in everyday tasks by gaining more and more experiences without the need for re-programming. In this paper, we review a set of previously published works and discuss advances in service robots from object perception to complex object manipulation and shed light on the current challenges and bottlenecks.

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Section: Assistive and Service Robotsmentioning

confidence: 99%

Section: Assistive and Service Robotsmentioning

confidence: 99%

Section: Robots Characteristicsmentioning

confidence: 99%

See 1 more Smart Citation

The State of Lifelong Learning in Service Robots:

Kasaei

Melsen

Beers

et al. 2021

J Intell Robot Syst

View full text Add to dashboard Cite

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“…The authors implemented the scale-invariant feature transform (SIFT) algorithm to extract the features of the object; then, a hierarchical k-means algorithm was used for the classification stage. In addition, Leroux et al in [ 90 ] proposed an algorithm for the SAM robot to locate and bring objects into the home as part of the ARMEN project. First, the SURF algorithm is used to extract the features.…”

Section: Algorithms Used For Objectsmentioning

confidence: 99%

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Montaño-Serrano

Jacinto‐Villegas

Vilchis-González

et al. 2021

Sensors

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Today, computer vision algorithms are very important for different fields and applications, such as closed-circuit television security, health status monitoring, and recognizing a specific person or object and robotics. Regarding this topic, the present paper deals with a recent review of the literature on computer vision algorithms (recognition and tracking of faces, bodies, and objects) oriented towards socially assistive robot applications. The performance, frames per second (FPS) processing speed, and hardware implemented to run the algorithms are highlighted by comparing the available solutions. Moreover, this paper provides general information for researchers interested in knowing which vision algorithms are available, enabling them to select the one that is most suitable to include in their robotic system applications.

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“…In the first of our sample scenarios we consider a relevant problem: (impaired) elderly care by robots [66][67][68]. In this scenario we consider that our libraries could be easily adapted to the specific sensory or cognitive requests of elderly people: loss of hearing, diminished vision, sensorimotor difficulties, memory losses, loss of expressivity, among others, something that explains why older people report fewer negative emotional experiences and greater emotional control [69][70][71].…”

Section: Elderly Carementioning

confidence: 99%

Allocentric Emotional Affordances in HRI: The Multimodal Binding

Vallverdú

Trovato

Jamone

2018

MTI

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The concept of affordance perception is one of the distinctive traits of human cognition; and its application to robots can dramatically improve the quality of human-robot interaction (HRI). In this paper we explore and discuss the idea of "emotional affordances" by proposing a viable model for implementation into HRI; which considers allocentric and multimodal perception. We consider "2-ways" affordances: perceived object triggering an emotion; and perceived human emotion expression triggering an action. In order to make the implementation generic; the proposed model includes a library that can be customised depending on the specific robot and application scenario. We present the AAA (Affordance-Appraisal-Arousal) model; which incorporates Plutchik's Wheel of Emotions; and we outline some numerical examples of how it can be used in different scenarios.Multimodal Technologies and Interact. 2018, 2, 78 2 of 20 emotional mapping based on 6 Ekman's emotions [17,18], and secondly it defends an egocentric spatial imagery, which involves a self-to-object representational system. Most of current studies on robotics and emotions are based on very basic aspects of emotional clues, mainly, visual ones (and based on a one-to-one direct connection with Ekman's thesis). Instead, here we want to consider the temporal nature of emotional modulations as well as the multidimensional elucidators of emotional values. At least two works are worth mentioning in these regards: Breazeal's emotional model for the robot Kismet [19] and the emotional model used for the robot . Both of them rely on a model based on three dimensions: while those are interesting approaches, we go beyond that architecture, extending to the concepts of affordances and learning.We have previously discussed some of the issues related to emotions [21,22], and with this paper we extend those discussions and provide a new way to consider emotional affordances in HRI. In our previous research we have studied how to identify a fundamental tree of basic emotions and how this mapping can be transformed into a dynamical semantic model, which includes temporal variations and a "logic of transitions" (that is, to model mood changes that look as real in average humans). The increase of the complexity of such model as well as the affordance approach has helped us to define the several parameters that must be considered for complex HRI modelling.In the present manuscript, we propose a viable model for implementation into HRI. We implement the case of 2-way affordances:

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ARMEN: Assistive robotics to maintain elderly people in natural environment

Cited by 17 publications

References 2 publications

The State of Lifelong Learning in Service Robots:

The State of Lifelong Learning in Service Robots:

Artificial Vision Algorithms for Socially Assistive Robot Applications: A Review of the Literature

Allocentric Emotional Affordances in HRI: The Multimodal Binding

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