Kinova Modular Robot Arms for Service Robotics Applications

Campeau-Lecours, Alexandre; Lamontagne, Hugo; Latour, Simon; Fauteux, Philippe; Maheu, Véronique; Boucher, François; Deguire, Charles; L'Ecuyer, Louis-Joseph Caron

doi:10.4018/ijrat.2017070104

Cited by 32 publications

(18 citation statements)

References 28 publications

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“…Terry Winograd -1971 high fidelity simulators for robotics (Todorov et al, 2012;Coumans andBai, 2016-2019;NVIDIA, 2019;Xiang et al, 2020) and the cost and availability of commodity hardware (Fitzgerald, 2013;Campeau-Lecours et al, 2019;Murali et al, 2019).…”

Section: Ws4: Embodiment and Actionmentioning

confidence: 99%

Experience Grounds Language

Bisk¹,

Holtzman²,

Thomason³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

169

136

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Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible effectiveness of language processing models to tackle tasks after being trained on text alone, successful linguistic communication relies on a shared experience of the world. It is this shared experience that makes utterances meaningful.Natural language processing is a diverse field, and progress throughout its development has come from new representational theories, modeling techniques, data collection paradigms, and tasks. We posit that the present success of representation learning approaches trained on large, text-only corpora requires the parallel tradition of research on the broader physical and social context of language to address the deeper questions of communication.

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Section: Ws4: Embodiment and Actionmentioning

confidence: 99%

Experience Grounds Language

Bisk¹,

Holtzman²,

Thomason³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

169

136

View full text Add to dashboard Cite

show abstract

“…Interestingly, there is a clear subgroup in these robotic systems that is focused on the integration of robotic arms into the wheelchair [ 21 , 22 , 23 ]. In [ 24 ] we even can find a commercialized solution able to help with some ADLs. However, these solutions are specific for users with important restrictions in mobility, and their cost is far from being affordable for the general public.…”

Section: Related Workmentioning

confidence: 99%

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

López-Sastre

Baptista-Ríos

Acevedo-Rodríguez

et al. 2021

IJERPH

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In this paper, we present a new low-cost robotic platform that has been explicitly developed to increase children with neurodevelopmental disorders’ involvement in the environment during everyday living activities. In order to support the children and youth with both the sequencing and learning of everyday living tasks, our robotic platform incorporates a sophisticated online action detection module that is capable of monitoring the acts performed by users. We explain all the technical details that allow many applications to be introduced to support individuals with functional diversity. We present this work as a proof of concept, which will enable an assessment of the impact that the developed technology may have on the collective of children and youth with neurodevelopmental disorders in the near future.

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“…In the upper part of Figure 2, we illustrate our physical kitchen table system setup, which consists of the following devices: 1) a Kinova Jaco light-weight manipulator (Campeau-Lecours et al, 2019), 2) a Microsoft Kinect2 RGB-D sensors, and 3) a dedicated PC with an Intel © Core ™ i7-6700 processor and an Nvidia GeForce GTX 970 graphics card.…”

Section: System Descriptionmentioning

confidence: 99%

Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations

Can¹,

Martires²,

Persson³

et al. 2019

Proceedings of the Combined Workshop on Spatial Language Understanding (

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Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot's world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.

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Kinova Modular Robot Arms for Service Robotics Applications

Cited by 32 publications

References 28 publications

Experience Grounds Language

Experience Grounds Language

A Low-Cost Assistive Robot for Children with Neurodevelopmental Disorders to Aid in Daily Living Activities

Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations

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