FORGE: 不確実性下での接触の多いマニピュレーションのための力誘導探索
FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
力閾値とダイナミクスランダム化を組み合わせてシミュレーションで学習し、姿勢の不確実性がある実機でも力に基づいて接触の多い作業を安全に遂行できる方策を実現した。
著者: Michael Noseworthy, Bingjie Tang, Bowen Wen, Ankur Handa, Chad Kessens, Nicholas Roy, Dieter Fox, Fabio Ramos, Yashraj Narang, Iretiayo Akinola
分類: cs.RO
原文アブストラクト
We present FORGE, a method for sim-to-real transfer of force-aware manipulation policies in the presence of significant pose uncertainty. During simulation-based policy learning, FORGE combines a force threshold mechanism with a dynamics randomization scheme to enable 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 avoiding aggressive and unsafe behaviour, regardless of the controller gains. Additionally, FORGE policies predict task success, enabling efficient termination and autonomous tuning of the force threshold. We show that FORGE can be used to learn a variety of robust contact-rich policies, including the forceful insertion of snap-fit connectors. We further demonstrate the multistage 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/.
