Multifingered Grasping Based on Multimodal Reinforcement Learning

Abstract

In this work, we solve the multifingered grasping problem using multimodal reinforcement learning. The simulation and real robot experiments with dedicated initial grasping poses show that our method outperforms hard-coded closing finger motion and the agent with fewer modalities in the grasp success rate for seen and unseen objects.

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Bibtex

@article{liang2022,
  author  = {Liang, Hongzhuo and Cong, Lin and Hendrich, Norman and Li, Shuang and Sun, Fuchun and Zhang, Jianwei},
  journal = {IEEE Robotics and Automation Letters (RA-L)},
  title   = {Multifingered Grasping Based on Multimodal Reinforcement Learning},
  year    = {2022},
  volume  = {7},
  number  = {2},
  pages   = {1174-1181},
  doi     = {10.1109/LRA.2021.3138545}
}