FORGE：不确定性下鲁棒接触丰富操作的力量引导探索
FORGE: Force-Guided Exploration for Robust Contact-Rich Manipulation under Uncertainty
AI总结：
FORGE通过力阈值机制与动力学随机化实现力感知操作策略的仿真到现实鲁棒迁移，在显著位姿不确定性下完成卡扣插入和行星齿轮多阶段装配等接触丰富任务。
摘要：
我们提出FORGE，一种在显著位姿不确定性存在的情况下，实现力感知操作策略的仿真到现实迁移的方法。在基于仿真的策略学习过程中，FORGE将力阈值机制与动力学随机化方案相结合，使学习到的策略能够鲁棒地迁移到真实机器人上。在部署时，FORGE策略以最大允许力为条件，自适应地执行接触丰富任务，同时避免激进和不安全的行为，无论控制器增益如何。此外，FORGE策略预测任务成功，从而实现高效终止和力阈值的自主调节。我们证明FORGE可用于学习多种鲁棒的接触丰富策略，包括卡扣式连接器的强力插入。我们进一步展示了行星齿轮系统的多阶段装配，这要求在三个装配任务中取得成功：螺母螺纹拧紧、插入和齿轮啮合。项目网站可访问https://noseworm.github.io/forge/。
英文摘要：
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/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
