Unsupervised feature learning for classifying dynamic tactile events using sparse coding

Roberge, Jean-Philippe; Rispal, Samuel; Wong, Tony; Duchaine, Vincent

doi:10.1109/icra.2016.7487428

Cited by 27 publications

(13 citation statements)

References 17 publications

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“…• Sparse Coding. This is a variant [50] of ST-HMP features [41]. These features are learned using dictionary learning and sparse coding on the spectrogram of 1D time series of tactile signals.…”

Section: A Learning Haptic Featuresmentioning

confidence: 99%

Learning to Grasp Without Seeing

Murali

Gandhi

et al. 2020

Springer Proceedings in Advanced Robotics

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Can a robot grasp an unknown object without seeing it? In this paper, we present a tactile-sensing based approach to this challenging problem of grasping novel objects without prior knowledge of their location or physical properties. Our key idea is to combine touch based object localization with tactile based regrasping. To train our learning models, we created a large-scale grasping dataset, including more than 30K RGB frames and over 2.8 million tactile samples from 7800 grasp interactions of 52 objects. To learn a representation of tactile signals, we propose an unsupervised auto-encoding scheme, which shows a significant improvement of 4-9% over prior methods on a variety of tactile perception tasks. Our system consists of two steps. First, our touch localization model sequentially "touch-scans" the workspace and uses a particle filter to aggregate beliefs from multiple hits of the target. It outputs an estimate of the object's location, from which an initial grasp is established. Next, our re-grasping model learns to progressively improve grasps with tactile feedback based on the learned features. This network learns to estimate grasp stability and predict adjustment for the next grasp. Re-grasping thus is performed iteratively until our model identifies a stable grasp. Finally, we demonstrate extensive experimental results on grasping a large set of novel objects using tactile sensing alone. Furthermore, when applied on top of a vision-based policy, our re-grasping model significantly boosts the overall accuracy by 10.6%. We believe this is the first attempt at learning to grasp with only tactile sensing and without any prior object knowledge. For supplementary video and dataset see: cs.cmu.edu/GraspingWithoutSeeing.

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Section: A Learning Haptic Featuresmentioning

confidence: 99%

Learning to Grasp Without Seeing

Murali

Gandhi

et al. 2020

Springer Proceedings in Advanced Robotics

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“…For instance, researchers have used tactile sensors to recognize object textures [8], and to detect object slippage [9], [10], [11]. Another component would be the ability to evaluate grasp stability, and use this knowledge to predict grasp failure.…”

Section: Introductionmentioning

confidence: 99%

Grasp stability assessment through unsupervised feature learning of tactile images

Cockbum

Roberge

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Grasping tasks have always been challenging for robots, despite recent innovations in vision-based algorithms and object-specific training. If robots are to match human abilities and learn to pick up never-before-seen objects, they must combine vision with tactile sensing. This paper present a novel way to improve robotic grasping: by using tactile sensors and an unsupervised feature-learning approach, a robot can find the common denominators behind successful and failed grasps, and use this knowledge to predict whether a grasp attempt will succeed or fail. This method is promising as it uses only high-level features from two tactile sensors to evaluate grasp quality, and works for the training set as well as for new objects. In total, using a total of 54 different objects, our system recognized grasp failure 83.70% of time.

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“…An application of dynamic sensing can be found in the work of Roberge et al [13], which used these data for classifying tactile events. Dynamic sensing has also been used to identify the texture of the contact surface [17].…”

Section: Multimodal Capacitive Sensormentioning

confidence: 99%

“…Capacitive sensors can now perform both static and dynamic sensing, because new integrated circuits (ICs) enable the sensor's electronic circuit to process the additional data needed for dynamic sensing. As a result, such sensors can now classify contact events [13].…”

Section: Introductionmentioning

confidence: 99%

A highly sensitive multimodal capacitive tactile sensor

Maslyczyk

Roberge

et al. 2017

2017 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-As technology develops, manufacture process becomes more and more automated using robots. There is demand for high performance tactile sensor which can support robotic grippers in manipulation tasks especially for unstructured flexible objects. Despite the efforts that have been spent, the fabrication process of those functional sensor remains complicated due to their requirement of specialized materials and equipment. The proposed multimodal sensor overcomes the difficulty by enhancing the electrical and mechanical design therefore simplifying the manufacture steps. In this version, static and dynamic sensing are integrated in the same layer of capacitive sensor with direct written microstructured dielectric. This structure allows it to have large range of force sensing as well as the ability of detecting contact events such as slippage or losing of contact.

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Unsupervised feature learning for classifying dynamic tactile events using sparse coding

Cited by 27 publications

References 17 publications

Learning to Grasp Without Seeing

Learning to Grasp Without Seeing

Grasp stability assessment through unsupervised feature learning of tactile images

A highly sensitive multimodal capacitive tactile sensor

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