Local implicit surface estimation for haptic exploration

Ottenhaus, Simon; Miller, Martin; Schiebener, David; Vahrenkamp, Nikolaus; Asfour, Tamim

doi:10.1109/humanoids.2016.7803372

Cited by 27 publications

(14 citation statements)

References 19 publications

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“…Humans use many exploration procedures to obtain knowledge about objects [30] -lateral motions, pressure, enclosure, contour following, object's part motion tests, and affordance tests. Inspired by these findings, many tactile-based exploration methods have been developed for robots to estimate important parameters including contact level information such as local geometry [66] and material properties [97], as well as object level information such as shape [156], [157] and mass information [123]. In addition to the analytical approaches, recently some researchers were proposing the data-driven approaches to learn the contact geometry and object's properties [75] [159] using large tactile dataset.…”

Section: A Tactile Explorationmentioning

confidence: 99%

A Review of Tactile Information: Perception and Action Through Touch

Kroemer

et al. 2020

IEEE Trans. Robot.

209

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Tactile sensing is a key sensor modality for robots interacting with their surroundings. These sensors provide a rich and diverse set of data signals that contain detailed information collected from contacts between the robot and its environment. The data is however not limited to individual contacts and can be used to extract a wide range of information about the objects in the robots environment as well as the robots own actions during the interactions.In this paper, we provide an overview of tactile information and its applications in robotics. We present a hierarchy consisting of raw, contact, object, and action levels to structure the tactile information, with higher-level information often building upon lower-level information. We discuss different types of information that can be extracted at each level of the hierarchy. The paper also includes an overview of different types of robot applications and the types of tactile information that they employ.The paper concludes with a discussion of tactile-based computational framework and future tactile applications which are still beyond current robot's capabilities.

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Section: A Tactile Explorationmentioning

confidence: 99%

A Review of Tactile Information: Perception and Action Through Touch

Kroemer

et al. 2020

IEEE Trans. Robot.

209

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“…There has also been work on estimating the location of objects using tactile sensing [25,26,27,28,29,30]. While some of these works use tactile sensing only as an indicator of contact [26], others such as Aggarwal and Kirchner [30] or Pezzementi et al [28] aim to recover both the object motion and its shape.…”

Section: Related Workmentioning

confidence: 99%

Tactile Mapping and Localization from High-Resolution Tactile Imprints

Bauzá

Canal

Rodríguez

2019

2019 International Conference on Robotics and Automation (ICRA)

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This work studies the problem of shape reconstruction and object localization using a vision-based tactile sensor, GelSlim. The main contributions are the recovery of local shapes from contact, an approach to reconstruct the tactile shape of objects from tactile imprints, and an accurate method for object localization of previously reconstructed objects. The algorithms can be applied to a large variety of 3D objects and provide accurate tactile feedback for in-hand manipulation.Results show that by exploiting the dense tactile information we can reconstruct the shape of objects with high accuracy and do on-line object identification and localization, opening the door to reactive manipulation guided by tactile sensing. We provide videos and supplemental information in the project's website web.mit.edu/mcube/research/tactile localization.html.

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“…For shape reconstruction in particular, tactile data have also been exploited for both local [28] [29] and global shape completion [30], [31], sometimes in a bimanual setting [32]. In recent years, researchers started to use active learning for shape reconstruction from tactile sensing [33], [34], [35], [36].…”

Section: B Tactile Sensing For Shape Reconstructionmentioning

confidence: 99%

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

Wang

Sun

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

106

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Perceiving accurate 3D object shape is important for robots to interact with the physical world. Current research along this direction has been primarily relying on visual observations. Vision, however useful, has inherent limitations due to occlusions and the 2D-3D ambiguities, especially for perception with a monocular camera. In contrast, touch gets precise local shape information, though its efficiency for reconstructing the entire shape could be low. In this paper, we propose a novel paradigm that efficiently perceives accurate 3D object shape by incorporating visual and tactile observations, as well as prior knowledge of common object shapes learned from large-scale shape repositories. We use vision first, applying neural networks with learned shape priors to predict an object's 3D shape from a single-view color image. We then use tactile sensing to refine the shape; the robot actively touches the object regions where the visual prediction has high uncertainty. Our method efficiently builds the 3D shape of common objects from a color image and a small number of tactile explorations (around 10). Our setup is easy to apply and has potentials to help robots better perform grasping or manipulation tasks on real-world objects.

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Local implicit surface estimation for haptic exploration

Cited by 27 publications

References 19 publications

A Review of Tactile Information: Perception and Action Through Touch

A Review of Tactile Information: Perception and Action Through Touch

Tactile Mapping and Localization from High-Resolution Tactile Imprints

3D Shape Perception from Monocular Vision, Touch, and Shape Priors

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