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Robotic cutting, or milling, plays a significant role in applications such asdisassembly, decommissioning, and demolition. Planning and control of cuttingin real-world scenarios in uncertain environments is a complex task, with thepotential to benefit from simulated training environments. This letter focuseson sim-to-real transfer for robotic cutting policies, addressing the need foreffective policy transfer from simulation to practical implementation. Weextend our previous domain generalisation approach to learning cutting tasksbased on a mechanistic model-based simulation framework, by proposing a hybridapproach for sim-to-real transfer based on a milling process force model andresidual Gaussian process (GP) force model, learned from either single ormultiple real-world cutting force examples. We demonstrate successfulsim-to-real transfer of a robotic cutting policy without the need forfine-tuning on the real robot setup. The proposed approach autonomously adaptsto materials with differing structural and mechanical properties. Furthermore,we demonstrate the proposed method outperforms fine-tuning or re-trainingalone.

Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force model