• Corpus ID: 239024560

Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space

@article{Ulmer2021LearningRM,
  title={Learning Robotic Manipulation Skills Using an Adaptive Force-Impedance Action Space},
  author={Maximilian Ulmer and Elie Aljalbout and Sascha Schwarz and Sami Haddadin},
  journal={ArXiv},
  year={2021},
  volume={abs/2110.09904}
}
Intelligent agents must be able to think fast and slow to perform elaborate manipulation tasks. Reinforcement Learning (RL) has led to many promising results on a range of challenging decision-making tasks. However, in real-world robotics, these methods still struggle, as they require large amounts of expensive interactions and have slow feedback loops. On the other hand, fast human-like adaptive control methods can optimize complex robotic interactions, yet fail to integrate multimodal… 

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