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BiGSiD: bionic grasping with edge-AI slip detection

  • Object grasping is a crucial task for robots, inspired by nature, where humans can flexibly grasp any object and detect whether it is slipping from grasp or not, more by the sense of touch than vision. In this work we present a bionic gripper with an Edge-AI device that is able to dexterously grasp the handled objects, sense and predict their slippage. In this paper, a bionic gripper with tactile sensors and a time-of-flight sensor is developed. We propose a LSTM model which is used to detect (incipient) slip/slippage, where a 6 degree-of-freedom robot manipulator is used for data collection and testing. The aim of this paper is to develop an efficient slip detection system which we can deploy on the edge device on our gripper, so it can be a stand-alone product that can be attached to almost any robotic manipulator. We have collected a dataset, trained the model and achieved a slip detection accuracy of 95.34%. Due to the efficiency of our model we were able to implement the slip detection on an edge device. We use the Nvidia Jetson AGX Orin development board to show the inference/prediction in a real-time scenario. We demonstrate in the our experiments how the on-gripper slip detection capability allows more robust grasping as the grip force is adjusted in response to a slippage.

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Author of HS ReutlingenNassar, Youssef; Radke, Mario; Gopal, Atmaraaj; Knöller, Tobias; Weber, Thomas; Rätsch, Matthias
Erschienen in:Robotics, computer vision and intelligent systems : 4th international conference, ROBOVIS 2024, Rome, Italy, February 25–27, 2024, proceedings
Place of publication:Cham
Editor:Joaquim Filipe, Juha Röning
Document Type:Conference proceeding
Publication year:2024
Tag:AI-edge-device; LSTM; slip detection; tactile sensor
Page Number:12
First Page:152
Last Page:163
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:004 Informatik
Open access?:Nein
Licence (German):License Logo  In Copyright - Urheberrechtlich geschützt