Principal Components of Touch

Manipulation tasks often require robots to be continuously in contact with an object. Therefore tactile perception systems need to handle continuous contact data. Shear deformation causes the tactile sensor to output path-dependent readings in contrast to discrete contact readings. As such, in some continuous-contact tasks, sliding can be regarded as a disturbance over the sensor signal. Here we present a shearinvariant perception method based on principal component analysis (PCA) which outputs the required information about the environment despite sliding motion. A compliant tactile sensor (the TacTip) is used to investigate continuous tactile contact. First, we evaluate the method offline using test data collected whilst the sensor slides over an edge. Then, the method is used within a contour-following task applied to 6 objects with varying curvatures; all contours are successfully traced. The method demonstrates generalisation capabilities and could underlie a more sophisticated controller for challenging manipulation or exploration tasks in unstructured environments.

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“…We use the sensitivity measure S introduced in Aquilina et al [22] to remove the indistinguishable data points. The sensitivity is defined by…”

Section: B Algorithmsmentioning

confidence: 99%

Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor

Aquilina

Barton

Lepora

2019

2019 International Conference on Robotics and Automation (ICRA)

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“…However, those methods did not extend well from discrete to continuous and dynamic environments. Consequently, techniques interpolating over continuously-labelled data were tried, such as Gaussian process regression to control sliding motion around 2D contours [91], [92] and polynomial regression to smoothly control a multi-fingered grasp [74]. The large amount of training data can be an issues in some circumstances, and so online learning using Gaussian Process latent variable models has also been explored [76], [95].…”

Section: B Progress In Tactile Capabilitiesmentioning

confidence: 99%

Soft Biomimetic Optical Tactile Sensing with the TacTip: A Review

Lepora

2021

Preprint

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<div>Reproducing the capabilities of the human sense of touch in machines is an important step in enabling robot manipulation to have the ease of human dexterity. A combination of robotic technologies will be needed, including soft robotics, biomimetics and the high-resolution sensing offered by optical tactile sensors. This combination is considered here as a SoftBOT (Soft Biomimetic Optical Tactile) sensor. This article reviews the BRL TacTip as a prototypical example of such a sensor. Topics include the relation between artificial skin morphology and the transduction principles of human touch, the nature and benefits of tactile shear sensing, 3D printing for fabrication and integration into robot hands, the application of AI to tactile perception and control, and the recent step-change in capabilities due to deep learning. This review consolidates those advances from the past decade to indicate a path for robots to reach human-like dexterity.</div><div><br></div>

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“…Alternatives to Vision: Various sensors have been used for in-hand pose estimation, such as proprioception [23], [24], and contact/force sensing [25], [26], [27]. Such sensors have also been combined with vision to decrease uncertainty [28], [29], [30], [31], [32], [33], [34], [35].…”

Section: Related Workmentioning

confidence: 99%

Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands

Wen,

Mitash,

Soorian

et al. 2020

Preprint

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Many manipulation tasks, such as placement or within-hand manipulation, require the object's pose relative to a robot hand. The task is difficult when the hand significantly occludes the object. It is especially hard for adaptive hands, for which it is not easy to detect the finger's configuration. In addition, RGB-only approaches face issues with texture-less objects or when the hand and the object look similar. This paper presents a depth-based framework, which aims for robust pose estimation and short response times. The approach detects the adaptive hand's state via efficient parallel search given the highest overlap between the hand's model and the point cloud. The hand's point cloud is pruned and robust global registration is performed to generate object pose hypotheses, which are clustered. False hypotheses are pruned via physical reasoning. The remaining poses' quality is evaluated given agreement with observed data. Extensive evaluation on synthetic and real data demonstrates the accuracy and computational efficiency of the framework when applied on challenging, highly-occluded scenarios for different object types. An ablation study identifies how the framework's components help in performance. This work also provides a dataset for in-hand 6D object pose estimation. Code and dataset are available at: https://github. com/wenbowen123/icra20-hand-object-pose

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Principal Components of Touch

Cited by 15 publications

References 21 publications

Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor

Shear-invariant Sliding Contact Perception with a Soft Tactile Sensor

Soft Biomimetic Optical Tactile Sensing with the TacTip: A Review

Robust, Occlusion-aware Pose Estimation for Objects Grasped by Adaptive Hands

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