Imitation Learning for Sim-To-Real Adaptation of Robotic Cutting Policies Based on Residual Gaussian Process Disturbance Force Model

Robotic cutting, a crucial task in applications such as disassembly and decommissioning, faces challenges due to uncertainties in real-world environments. This paper presents a novel approach to enhance sim-to-real transfer of robotic cutting policies, leveraging a hybrid method integrating Gaussian process (GP) regression to model disturbance forces encountered during cutting tasks. By learning from a limited number of real-world trials, our method captures residual pro- cess dynamics, enabling effective adaptation to diverse materials without the need for fine-tuning on physical robots. Key to our approach is the utilisation of imitation learning, where expert actions in the uncorrected simulation are paired with GP- corrected observations. This pairing aligns action distributions between simulated and real-world domains, facilitating robust policy transfer. We illustrate the efficacy of our method through real world cutting trials in autonomously adapting to diverse material properties; our method surpasses re-training, while providing similar benefits to fine-tuning in real-world cutting scenarios. Notably, policies transferred using our approach exhibit enhanced resilience to noise and disturbances, while maintaining fidelity to expert behaviours from the source domain.