Divide et Impera: Learning impedance families for peg-in-hole assembly
<|reference_start|>Divide et Impera: Learning impedance families for peg-in-hole assembly: This paper addresses robotic peg-in-hole assembly using the framework of Elementary Dynamic Actions (EDA). Inspired by motor primitives in neuromotor control research, the method leverages three primitives: submovements, oscillations, and mechanical impedances (e.g., stiffness and damping), combined via a Norton equivalent network model. By focusing on impedance parameterization, we explore the adaptability of EDA in contact-rich tasks. Experimental results, conducted on a real robot setup with four different peg types, demonstrated a range of successful impedance parameters, challenging conventional methods that seek optimal parameters. We analyze our data in a lower-dimensional solution space. Clustering analysis shows the possibility to identify different individual strategies for each single peg, as well as common strategies across all pegs. A neural network model, trained on the experimental data, accurately predicted successful impedance parameters across all pegs. The practical utility of this work is enhanced by a success-predictor model and the public availability of all code and CAD files. These findings highlight the flexibility and robustness of EDA; show multiple equally-successful strategies for contact-rich manipulation; and offer valuable insights and tools for robotic assembly programming.<|reference_end|>
@article{lachner2024divide,
    title={Divide et Impera: Learning impedance families for peg-in-hole assembly},
    author={Johannes Lachner, Federico Tessari, A. Michael West Jr., Moses C. Nah,
  Neville Hogan},
    journal={arXiv preprint arXiv:2410.01054},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01054},
    primaryClass={cs.RO}
}