Exploring and Selecting Supershapes in Virtual Reality with Line, Quad, and Cube Shaped Widgets

Nicolau, Francisco; Gielis, Johan; Simeone, Adalberto L.; Lopes, Daniel Simões

doi:10.1109/vr51125.2022.00019

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…The objects or stimuli were generated using Gielis' parametric model, also known as Supershapes, [Nicolau et al(2022), Gielis(2003)], which have been used in previous studies in a similar context [Fougerolle et al(2007), Garrofé et al(2021)]. It is defined by the following formula:…”

Section: Stimulimentioning

confidence: 99%

Bridging Realities: Training visuo-haptic object recognition models for robots using 3D virtual simulations

Ruiz,

de Jesús,

Serrano

et al. 2024

Preprint

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This paper proposes an approach for training visuo-haptic object recognition models for robots using synthetic datasets generated by 3D virtual simulations. In robotics, where visual object recognition has witnessed considerable progress due to an abundance of image datasets, the scarcity of diverse haptic samples has resulted in a noticeable gap in research on machine learning incorporating the haptic sense. Our proposed methodology addresses this challenge by utilizing 3D virtual simulations to create realistic synthetic datasets, offering a scalable and cost-effective solution to integrate haptic and visual cues for object recognition seamlessly. Acknowledging the importance of multimodal perception, particularly in robotic applications, our research not only closes the existing gap but envisions a future where intelligent agents possess a holistic understanding of their environment derived from both visual and haptic senses. Our experiments show that synthetic datasets can be used for training object recognition in haptic and visual modes by incorporating noise, performing some preprocessing, data augmentation, or domain adaptation. This work contributes to the advancement of multimodal machine learning towards a more nuanced and comprehensive robotic perception.

show abstract

Section: Stimulimentioning

confidence: 99%

Bridging Realities: Training visuo-haptic object recognition models for robots using 3D virtual simulations

Ruiz,

de Jesús,

Serrano

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…The objects or stimuli were generated using Gielis' parametric model, also known as Supershapes, [18,19], which have been used in previous studies in a similar context [20,21]. It is defined by the following formula:…”

Section: Stimulimentioning

confidence: 99%

Bridging realities: training visuo-haptic object recognition models for robots using 3D virtual simulations

Ruiz,

de Jesús,

Serrano

et al. 2024

Vis Comput

View full text Add to dashboard Cite

This paper proposes an approach for training visuo-haptic object recognition models for robots using synthetic datasets generated by 3D virtual simulations. In robotics, where visual object recognition has witnessed considerable progress due to an abundance of image datasets, the scarcity of diverse haptic samples has resulted in a noticeable gap in research on machine learning incorporating the haptic sense. Our proposed methodology addresses this challenge by utilizing 3D virtual simulations to create realistic synthetic datasets, offering a scalable and cost-effective solution to integrate haptic and visual cues for object recognition seamlessly. Acknowledging the importance of multimodal perception, particularly in robotic applications, our research not only closes the existing gap but envisions a future where intelligent agents possess a holistic understanding of their environment derived from both visual and haptic senses. Our experiments show that synthetic datasets can be used for training object recognition in haptic and visual modes by incorporating noise, performing some preprocessing, data augmentation, or domain adaptation. This work contributes to the advancement of multimodal machine learning toward a more nuanced and comprehensive robotic perception.

show abstract

Exploring and Selecting Supershapes in Virtual Reality with Line, Quad, and Cube Shaped Widgets

Cited by 2 publications

References 41 publications

Bridging Realities: Training visuo-haptic object recognition models for robots using 3D virtual simulations

Bridging Realities: Training visuo-haptic object recognition models for robots using 3D virtual simulations

Bridging realities: training visuo-haptic object recognition models for robots using 3D virtual simulations

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