Virtual Haptic System for Shape Recognition Based on Local Curvatures

Garrofé, Guillem; Parés, Carlota; Gutierrez, Anna; Ruiz, Conrado; Serra, Gerard; Miralles, David

doi:10.1007/978-3-030-89029-2_3

Cited by 5 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

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 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

Self Cite

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

“…In this study, we use an artificial haptic capture system that captures raw curvatures from different 3D stimuli in a virtual environment, described in [16]. To capture local curvature information, we employ three equally spaced end-effectors that collide with the surface of the stimulus, obtaining three relative time values with respect to the first capture.…”

Section: Haptic System and Stimulimentioning

confidence: 99%

“…Next, we train and test an XGBoost classifier for recognition purposes. For a detailed explanation of our XGBoost model, including the parameters used for training, please refer to the following paper [16]. To gain a deeper understanding of the relationships between stimuli, we intentionally limited the number of test samples to just 5 haptic samples, in-Figure 1.…”

Section: Classification and Confusionmentioning

confidence: 99%

Internal Representation and Shepard’s Law in an Artificial Haptic System

Serrano,

Miralles

2023

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

In nature, the generalization in object recognition is based not in the stimuli space but the internal representation as Shepard proposed in his Universal Law of Generalization. The fact that this law applies to many biological system gives it its universal character. The extension of this universality to artificial systems remains to be studied. In this paper, we present how an artificial agent generates the internal representation of a collection of stimuli from a previous experience. More-over, from the internal representation and the classification of the artificial agent, we are already able to verify Shepard’s law. It should be noted that the presented methodology is independent of the classifier. In our case, we have applied it to an artificial system that captures haptic information from a collection of stimuli. We have verified that the relationship between the perceived distances in the internal representation and the probability of confusion between stimuli follows Shepard’s law. Verifying compliance with this law in artificial systems and studying its implications can be relevant to understanding generalization in learning.

show abstract

Virtual Haptic System for Shape Recognition Based on Local Curvatures

Cited by 5 publications

References 20 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

Internal Representation and Shepard’s Law in an Artificial Haptic System

Contact Info

Product

Resources

About