Tactile Mapping and Localization from High-Resolution Tactile Imprints

Bauzá, Maria; Canal, Oleguer; Rodríguez, Alberto

doi:10.48550/arxiv.1904.10944

Cited by 2 publications

(2 citation statements)

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“…In the case of visuotactile sensors, the rendered depth maps can be of sufficiently high resolution to be used directly for inhand pose estimation [15]. Direct depth registration methods such as Iterative Closest Point (ICP) can be employed for local pose estimation [2] given a sufficiently close initial guess that can either be learned [16] or be obtained from methods that can compute the contact-patch resulting from contact with a tactile sensor [3]. Also, external depth sensor information can be fused with tactile depth [17].…”

Section: In-hand Pose Estimationmentioning

confidence: 99%

Soft-Bubble grippers for robust and perceptive manipulation

Kuppuswamy¹,

Alspach²,

Uttamchandani³

et al. 2020

Preprint

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Manipulation in cluttered environments like homes requires stable grasps, precise placement and robustness against external contact. We present the Soft-bubble gripper system with a highly compliant gripping surface and dense-geometry visuotactile sensing, capable of multiple kinds of tactile perception. We first present various mechanical design advances and a fabrication technique to deposit custom patterns to the internal surface of the sensor that enable tracking of shearinduced displacement of the manipuland. The depth maps output by the internal imaging sensor are used in an in-hand proximity pose estimation framework -the method better captures distances to corners or edges on the manipuland geometry. We also extend our previous work on tactile classification and integrate the system within a robust manipulation pipeline for cluttered home environments. The capabilities of the proposed system are demonstrated through robust execution multiple real-world manipulation tasks. A video of the system in action can be found here.

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Section: In-hand Pose Estimationmentioning

confidence: 99%

Soft-Bubble grippers for robust and perceptive manipulation

Kuppuswamy¹,

Alspach²,

Uttamchandani³

et al. 2020

Preprint

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“…Izatt [12] fuses tactile and visual perception by interpreting tactile imprints as local 3D pointclouds within a Kalman filter framework for object pose estimation. More recently, Bauza [13] develops a tactile based pose estimation algorithm that exploits a high resolution tactile map of the object to localize tactile imprints. [14] first studied the use of robotic palms as a way to achieve richer manipulation skills by exploiting the mobility of robotic manipulators.…”

Section: Related Workmentioning

confidence: 99%

Tactile Dexterity: Manipulation Primitives with Tactile Feedback

Hogan¹,

Ballester²,

Dong³

et al. 2020

Preprint

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This paper develops closed-loop tactile controllers for dexterous manipulation with dual-arm robotic palms. Tactile dexterity is an approach to dexterous manipulation that plans for robot/object interactions that render interpretable tactile information for control. We divide the role of tactile control into two goals: 1) control the contact state between the endeffector and the object (contact/no-contact, stick/slip, forces) and 2) control the object state by tracking the object with a tactile-based state estimator.Key to this formulation is the decomposition of manipulation plans into sequences of manipulation primitives with simple mechanics and efficient planners. We consider the scenario of manipulating an object from an initial pose to a target pose on a flat surface while correcting for external perturbations and uncertainty in the initial pose of the object. We validate the approach with an ABB YuMi dual-arm robot and demonstrate the ability of the tactile controller to handle external perturbations.

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Tactile Mapping and Localization from High-Resolution Tactile Imprints

Cited by 2 publications

References 0 publications

Soft-Bubble grippers for robust and perceptive manipulation

Soft-Bubble grippers for robust and perceptive manipulation

Tactile Dexterity: Manipulation Primitives with Tactile Feedback

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