Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes

@article{Wu2019PixelAttentivePG,
  title={Pixel-Attentive Policy Gradient for Multi-Fingered Grasping in Cluttered Scenes},
  author={B. Wu and Iretiayo Akinola and P. Allen},
  journal={2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2019},
  pages={1789-1796}
}
  • B. Wu, Iretiayo Akinola, P. Allen
  • Published 2019
  • Computer Science
  • 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • Recent advances in on-policy reinforcement learning (RL) methods enabled learning agents in virtual environments to master complex tasks with high-dimensional and continuous observation and action spaces. However, leveraging this family of algorithms in multi-fingered robotic grasping remains a challenge due to large sim-to-real fidelity gaps and the high sample complexity of on-policy RL algorithms. This work aims to bridge these gaps by first reinforcement-learning a multi-fingered robotic… CONTINUE READING
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