Barometer-based Tactile Skin for Anthropomorphic Robot Hand

Kõiva, Risto; Schwank, Tobias; Walck, Guillaume; Meier, Martin; Haschke, Robert; Ritter, Helge

doi:10.1109/iros45743.2020.9341691

Cited by 17 publications

(13 citation statements)

References 19 publications

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“…Similarly, an ability to estimate the type and direction of slip would be valuable. Finally, we would like to examine the use of different barometric sensor configurations for tactile sensing, including flexible 'skin,' similar to [27].…”

Section: Discussionmentioning

confidence: 99%

“…However, like most other vision-based sensors, this technology is bulky, preventing it from being placed anywhere other than on the fingertips. In contrast, barometric sensors are compact and can be easily distributed on the palm of a hand [27]. A subset of methods for slip detection rely on an analysis of the vibration pattern induced by object slip, where the frequency of the vibration (due to material resonance) depends on the composition of the surfaces in contact.…”

Section: Related Workmentioning

confidence: 99%

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Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network

Grover,

Nadeau,

Grebe

et al. 2022

Preprint

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The ability to perceive object slip via tactile feedback enables humans to accomplish complex manipulation tasks including maintaining a stable grasp. Despite the utility of tactile information for many applications, tactile sensors have yet to be widely deployed in industrial robotics settings; part of the challenge lies in identifying slip and other events from the tactile data stream. In this paper, we present a learningbased method to detect slip using barometric tactile sensors. These sensors have many desirable properties including high durability and reliability, and are built from inexpensive, offthe-shelf components. We train a temporal convolution neural network to detect slip, achieving high detection accuracies while displaying robustness to the speed and direction of the slip motion. Further, we test our detector on two manipulation tasks involving a variety of common objects and demonstrate successful generalization to real-world scenarios not seen during training. We argue that barometric tactile sensing technology, combined with data-driven learning, is suitable for many manipulation tasks such as slip compensation.

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Section: Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network

Grover,

Nadeau,

Grebe

et al. 2022

Preprint

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show abstract

“…Tenzer and Jentfot [31,32] developed a versatile, low-cost, and sensitive tactile sensor using commercial off-the-shelf MEMS barometers and commercialized it as TakkTile [33]. It has been used in robotics by Ades et al [34], and Koiva et al [35] to sense grasping events using robotic grippers. The working principle of the MEMS barometer-based tactile sensor is the communication of surface contact pressure within a layer of rubber to the ventilation hole of the sensor and, thus, to the MEMS transducer.…”

Section: Detecting Toy Squeezingmentioning

confidence: 99%

Design and Development of Instrumented Toys for Social Interactive Assessment of Infant Cognitive Flexibility

Ramanathan¹,

Ariffin²,

Goh³

et al. 2022

Preprint

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The first years of an infant’s life represent a sensitive period for neurodevelopment and see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed instrumented toys as a new task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a Barometer and Inertial Measurement Unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provides a rich dataset describing the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition may be inferred. Such a tool potentially provides an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.

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“…Pressure drifting is a commonly observed issue in barometers. This is mainly caused by the coupling effects from the temperature, and the partial inelastic strain recovery of the gel when a large force is applied [34], [57]. The drifting is harmful because it reduces the signal-noise ratio, and thus may degrade the confidence in detecting contacts.…”

Section: Whisker Sensor Designmentioning

confidence: 99%

Active Multi-Object Exploration and Recognition via Tactile Whiskers

Xiao,

Xu,

et al. 2021

Preprint

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Robotic exploration under uncertain environments is challenging when optical information is not available. In this paper, we propose an autonomous solution of exploring an unknown task space based on tactile sensing alone. We first designed a whisker sensor based on MEMS barometer devices. This sensor can acquire contact information by interacting with the environment non-intrusively.This sensor is accompanied by a planning technique to generate exploration trajectories by using mere tactile perception. This technique relies on a hybrid policy for tactile exploration, which includes a proactive informative path planner for object searching, and a reactive Hopf oscillator for contour tracing. Results indicate that the hybrid exploration policy can increase the efficiency of object discovery.Last, scene understanding was facilitated by segmenting objects and classification. A classifier was developed to recognize the object categories based on the geometric features collected by the whisker sensor. Such an approach demonstrates the whisker sensor, together with the tactile intelligence, can provide sufficiently discriminative features to distinguish objects.

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Barometer-based Tactile Skin for Anthropomorphic Robot Hand

Cited by 17 publications

References 19 publications

Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network

Learning to Detect Slip with Barometric Tactile Sensors and a Temporal Convolutional Neural Network

Design and Development of Instrumented Toys for Social Interactive Assessment of Infant Cognitive Flexibility

Active Multi-Object Exploration and Recognition via Tactile Whiskers

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