A Robotic Grasping State Perception Framework With Multi-Phase Tactile Information and Ensemble Learning

Gang Yan*, Alexander Schmitz, Satoshi Funabashi, Sophon Somlor, Tito Pradhono Tomo, Shigeki Sugano

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Recently, tactile sensing has attracted increasing attention for robotic manipulation. Predicting the grasping stability before lifting objects and detecting the ongoing/onset of slip after lifting objects are two critical and widely studied tasks in robotic tactile manipulation. Previous methods focus on proposing novel neural networks (NN) architectures towards one of the above two tasks and did not consider that the two tasks are employed in two interconnected action-phases, i.e. grasping and lifting. Therefore, we firstly explore the possibility of constructing a multi-phase, multi-output framework to combine the stability prediction before lifting and the slip detection after lifting. Moreover, to improve the prediction/detection accuracy, we also proposed to explicitly ensemble different NN architectures using various methods, including attention mechanisms. Our experiments are done with 6 state-of-art NN architectures on two datasets including more than 3000 robotic grasps over 80 objects in total. Furthermore our proposals are tested in a real-time robot experiment with unseen objects. Our experimental results show that the proposed multi-phase, multi-output model exhibits more reliable and flexible performance than a single phase model. We also show that using the ensemble of different NN architectures is a viable and practical choice to boost the overall performance.

Original languageEnglish
Pages (from-to)6822-6829
Number of pages8
JournalIEEE Robotics and Automation Letters
Issue number3
Publication statusPublished - 2022 Jul 1


  • Deep learning in grasping and manipulation
  • grasping
  • perception for grasping and manipulation

ASJC Scopus subject areas

  • Mechanical Engineering
  • Control and Optimization
  • Artificial Intelligence
  • Human-Computer Interaction
  • Control and Systems Engineering
  • Computer Vision and Pattern Recognition
  • Biomedical Engineering
  • Computer Science Applications


Dive into the research topics of 'A Robotic Grasping State Perception Framework With Multi-Phase Tactile Information and Ensemble Learning'. Together they form a unique fingerprint.

Cite this