Robotic cutting, or milling, plays a significant role in applications such as disassembly, decommissioning, and demolition. Planning and control of cutting in real-world scenarios in uncertain environments is a complex task, with the potential to benefit from simulated training environments. This letter focuses on sim-to-real transfer for robotic cutting policies, addressing the need for effective policy transfer from simulation to practical implementation. We extend our previous domain generalisation approach to learning cutting tasks based on a mechanistic model-based simulation framework, by proposing a hybrid approach for sim-to-real transfer based on a milling process force model and residual Gaussian process (GP) force model, learned from either single or multiple real-world cutting force examples. We demonstrate successful sim-to-real transfer of a robotic cutting policy without the need for fine-tuning on the real robot setup. The proposed approach autonomously adapts to materials with differing structural and mechanical properties. Furthermore, we demonstrate the proposed method outperforms fine-tuning or re-training alone.

翻译：机器人切割（即铣削）在拆解、退役和拆除等应用中起着重要作用。在不确定环境下的实际场景中，规划和控制切割是一项复杂任务，而模拟训练环境有望为其提供助力。本文聚焦于机器人切割策略的仿真到现实迁移，旨在解决从仿真到实际实施的有效策略迁移需求。我们扩展了先前基于机理模型仿真框架学习切割任务的领域泛化方法，提出了一种基于铣削过程力模型和残差高斯过程（GP）力模型的混合式仿真到现实迁移方法，该残差GP力模型可通过单个或多个真实切割力示例学习。我们成功实现了机器人切割策略的仿真到现实迁移，无需在真实机器人平台上进行微调。所提方法能够自主适应具有不同结构和力学特性的材料。此外，我们证明所提方法在性能上优于单独使用微调或重新训练的方法。