Corpus ID: 237500490

Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation

@inproceedings{Davchev2020ResidualLF,
  title={Residual Learning from Demonstration: Adapting DMPs for Contact-rich Manipulation},
  author={Todor Davchev and Kevin Sebastian Luck and Michael Burke and Franziska Meier and Stefan Schaal and Subramanian Ramamoorthy},
  year={2020}
}
Manipulation skills involving contact and friction are inherent to many robotics tasks. Using the class of motor primitives for peg-in-hole like insertions, we study how robots can learn such skills. Dynamic Movement Primitives (DMP) are a popular way of extracting such policies through behaviour cloning (BC), but can struggle in the context of insertion. Policy adaptation strategies such as residual learning can help improve the overall performance of policies in the context of contact-rich… Expand

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