Self-supervised learning of object slippage: An LSTM model trained on low-cost tactile sensors

• This paper presents a combination of machine learning techniques for slip detection in grasping, based on temporal features collected by low-cost tactile sensors. A slippage is an event that is subsequent to prior micro-slippages that have occurred at hand-object contact. The method is based on the application of a sequential classification technique (a variant of recurrent neural networks known as long short-term memory networks or LSTMs), whereby time-series pressure readings from tactile sensors are classified as either slip or non-slip events. We also propose a novel method for autonomous labelling, removing the need for humans in the labelling process. Lastly, this paper proposes a new design for an adaptable wearable tactile sensing device that integrates non-expensive sensors. Our proposed method achieved high accuracy in the classification of slip and non-slip events, obtaining over 95% in offline classification and 89% in online classification using a Sawyer robot.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Robotteknik och automation (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Robotics (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

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