用数字孪生模拟软夹爪，提升欠驱动控制精度。
Development of a Multi-Fingered Soft Gripper Digital Twin for Machine Learning-based Underactuated Control
- 构建包含非线性、迟滞等特性的软夹爪数字孪生
- 通过强化学习找到最小化不确定性的最优运动速度
- 适合机器人控制与机器学习交叉研究者
软体机器人由柔性材料制成，因高自由度和灵活性导致动力学复杂。控制软体机器人面临显著挑战，尤其是欠驱动问题——输入数量少于自由度数。本研究旨在为多指软夹爪开发数字孪生系统，以推动欠驱动算法的发展。数字孪生设计捕捉软体机器人关键特性，如非线性、迟滞、不确定性及时变现象，确保其行为与真实软夹爪高度一致。不确定性通过蒙特卡洛方法模拟。基于该数字孪生环境，初步应用Q-learning算法，识别出使软体机器人不确定性最小化的最优运动速度，并成功模拟了欠驱动运动。该数字孪生为高级机器学习算法的训练提供了可靠平台。
原文摘要 · Abstract (English)
Soft robots, made from compliant materials, exhibit complex dynamics due to their flexibility and high degrees of freedom. Controlling soft robots presents significant challenges, particularly underactuation, where the number of inputs is fewer than the degrees of freedom. This research aims to develop a digital twin for multi-fingered soft grippers to advance the development of underactuation algorithms. The digital twin is designed to capture key effects observed in soft robots, such as nonlinearity, hysteresis, uncertainty, and time-varying phenomena, ensuring it closely replicates the behavior of a real-world soft gripper. Uncertainty is simulated using the Monte Carlo method. With the digital twin, a Q-learning algorithm is preliminarily applied to identify the optimal motion speed that minimizes uncertainty caused by the soft robots. Underactuated motions are successfully simulated within this environment. This digital twin paves the way for advanced machine learning algorithm training.
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