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

arXiv: https://arxiv.org/abs/2311.04096 · HTML: https://mlsanity.com/paper/2311.04096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Robot learning from demonstration, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 27 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Placed in

- Offline RL and imitation learning from data / [Robot learning from demonstration](https://mlsanity.com/offlinerl/robot-learning-from-demonstration/)

## Builds on (most cited first, 10 of 10)

- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Transfer from Simulation to Real World through Learning Deep Inverse Dynamics Model](https://mlsanity.com/paper/1610.03518/) (2016)
- [Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization](https://mlsanity.com/paper/2002.11635/) (2020)
- [ADATIME: A Benchmarking Suite for Domain Adaptation on Time Series Data](https://mlsanity.com/paper/2203.08321/) (2022)
- [Domain Adaptation In Reinforcement Learning Via Latent Unified State Representation](https://mlsanity.com/paper/2102.05714/) (2021)
- [Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2008.10224/) (2020)
- [Deep Spherical Manifold Gaussian Kernel for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2105.02089/) (2021)
- [One-Shot Domain-Adaptive Imitation Learning via Progressive Learning](https://mlsanity.com/paper/2204.11251/) (2022)
- [Learning robotic milling strategies based on passive variable operational space interaction control](https://mlsanity.com/paper/2304.01000/) (2023)
- [Gaussian Process Domain Experts for Model Adaptation in Facial Behavior Analysis](https://mlsanity.com/paper/1604.02917/) (2016)

## Abstract (arXiv)

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.