FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
<|reference_start|>FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty: We present FORGE, a method that enables sim-to-real transfer of contact-rich manipulation policies in the presence of significant pose uncertainty. FORGE combines a force threshold mechanism with a dynamics randomization scheme during policy learning in simulation, to enable the robust transfer of the learned policies to the real robot. At deployment, FORGE policies, conditioned on a maximum allowable force, adaptively perform contact-rich tasks while respecting the specified force threshold, regardless of the controller gains. Additionally, FORGE autonomously predicts a termination action once the task has succeeded. We demonstrate that FORGE can be used to learn a variety of robust contact-rich policies, enabling multi-stage assembly of a planetary gear system, which requires success across three assembly tasks: nut-threading, insertion, and gear meshing. Project website can be accessed at https://noseworm.github.io/forge/.<|reference_end|>
@article{noseworthy2024forge:,
    title={FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation
  under Uncertainty},
    author={Michael Noseworthy, Bingjie Tang, Bowen Wen, Ankur Handa, Nicholas
  Roy, Dieter Fox, Fabio Ramos, Yashraj Narang, Iretiayo Akinola},
    journal={arXiv preprint arXiv:2408.04587},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.04587},
    primaryClass={cs.RO}
}