残留ガウス過程外乱力モデルに基づくロボット切断方策のsim-to-real転移のための模倣学習
Imitation learning for sim-to-real transfer of robotic cutting policies based on residual Gaussian process disturbance force model
切削力の物理モデルと残留ガウス過程モデルを組み合わせ、実機での微調整なしにシミュレーションで学習したロボット切断方策を実環境へ転移させる手法を提案した。
著者: Jamie Hathaway, Rustam Stolkin, Alireza Rastegarpanah
分類: cs.RO
原文アブストラクト
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.
