Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies

@article{Mosbach2022EfficientRO,
  title={Efficient Representations of Object Geometry for Reinforcement Learning of Interactive Grasping Policies},
  author={Malte Mosbach and Sven Behnke},
  journal={2022 Sixth IEEE International Conference on Robotic Computing (IRC)},
  year={2022},
  pages={156-163}
}
  • Malte MosbachSven Behnke
  • Published 20 November 2022
  • Computer Science
  • 2022 Sixth IEEE International Conference on Robotic Computing (IRC)
Grasping objects of different shapes and sizes-a foundational, effortless skill for humans-remains a challenging task in robotics. Although model-based approaches can predict stable grasp configurations for known object models, they struggle to generalize to novel objects and often operate in a non-interactive open-loop manner. In this work, we present a reinforcement learning framework that learns the interactive grasping of various geometrically distinct real-world objects by continuously… 
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