実2シミュレーション動力学推定と強化学習によるトルク制御ロボットのSim2Real転送の強化
Enhancing Sim2Real Transfer for Torque-Controlled Robots through Real2Sim Dynamics Estimation and Reinforcement Learning
トルク制御ロボットアームのシミュレーションから実機へのポリシー転送を改善するため、実機の軌跡データから動力学パラメータを同定し、遺伝的アルゴリズムとドメインランダム化を組み合わせたReal2Sim2Realパイプラインを提案した。7自由度のFranka Emika Pandaロボットで検証し、追従精度とロバスト性が向上した。
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著者: Davide Bargellini, Alex Pasquali, Andrea Govoni, Riccardo Zanella, Gianluca Palli
分類: cs.RO, eess.SY
原文アブストラクト
Transferring reinforcement learning policies from simulation to Real-World robots remains a major challenge, particularly when dealing with low-level torque control, where even small modelling inaccuracies can lead to unstable or unsafe behaviours. In this work, we propose a Real2Sim2Real pipeline that improves Sim2Real transfer for torque-controlled robotic arms by combining trajectory matching, parameter optimization via genetic algorithms, and domain randomization. Using the 7-DOF Franka Emika Panda robot, we first identify friction, inertia, and gravity compensation parameters by minimizing the error between real and simulated joint trajectories. These calibrated dynamics are then used to train a TQC-based reinforcement learning agent in simulation. The trained policy is evaluated in both Gazebo and MuJoCo environments, and finally deployed on the real robot. Our results demonstrate a significant improvement in tracking accuracy and policy robustness after parameter tuning, with smooth policy transfer from simulation to the Real-World across multiple target-reaching tasks. This work highlights the effectiveness of accurate physical modelling in enabling stable and generalizable torque-based reinforcement learning policies.