📄 中文摘要
该研究提出了一种新的深度逆动力学模型，旨在实现仿真环境与现实世界之间的有效转移。通过深度学习技术，该模型能够从仿真数据中学习并生成准确的动力学预测，从而在真实世界中应用。研究中采用了多种数据集进行训练和验证，结果表明该模型在处理复杂动态系统时表现出色。该方法为机器人控制、自动驾驶等领域提供了新的解决方案，具有广泛的应用前景。
通过学习深度逆动力学模型实现从仿真到现实的转移
📄 English Summary
Transfer from Simulation to Real World through Learning Deep Inverse DynamicsModel
A novel deep inverse dynamics model is proposed to facilitate the effective transfer from simulation environments to the real world. Leveraging deep learning techniques, the model learns from simulation data to generate accurate dynamics predictions for real-world applications. Various datasets were utilized for training and validation, demonstrating the model's exceptional performance in handling complex dynamic systems. This approach offers new solutions for fields such as robotic control and autonomous driving, showcasing a wide range of potential applications.