FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
Abstract
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/.
- Publication:
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arXiv e-prints
- Pub Date:
- August 2024
- DOI:
- 10.48550/arXiv.2408.04587
- arXiv:
- arXiv:2408.04587
- Bibcode:
- 2024arXiv240804587N
- Keywords:
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- Computer Science - Robotics