Bridging the sim-to-real gap remains a fundamental challenge in robotics, as accurate dynamic parameter estimation is essential for reliable model-based control, realistic simulation, and safe deployment of manipulators. Traditional analytical approaches often fall short when faced with complex robot structures and interactions. Data-driven methods offer a promising alternative, yet conventional neural networks such as recurrent models struggle to capture long-range dependencies critical for accurate estimation. In this study, we propose a Transformer-based approach for dynamic parameter estimation, supported by an automated pipeline that generates diverse robot models and enriched trajectory data using Jacobian-derived features. The dataset consists of 8,192 robots with varied inertial and frictional properties. Leveraging attention mechanisms, our model effectively captures both temporal and spatial dependencies. Experimental results highlight the influence of sequence length, sampling rate, and architecture, with the best configuration (sequence length 64, 64 Hz, four layers, 32 heads) achieving a validation R2 of 0.8633. Mass and inertia are estimated with near-perfect accuracy, Coulomb friction with moderate-to-high accuracy, while viscous friction and distal link center-of-mass remain more challenging. These results demonstrate that combining Transformers with automated dataset generation and kinematic enrichment enables scalable, accurate dynamic parameter estimation, contributing to improved sim-to-real transfer in robotic systems

翻译：弥合仿真与现实的差距仍然是机器人学中的一个根本性挑战，因为精确的动态参数估计对于可靠的基于模型的控制、逼真的仿真以及机械臂的安全部署至关重要。面对复杂的机器人结构和交互作用，传统的解析方法往往表现不足。数据驱动方法提供了一种有前景的替代方案，然而传统的循环神经网络等模型难以捕捉对精确估计至关重要的长程依赖关系。在本研究中，我们提出了一种基于Transformer的动态参数估计方法，并辅以一个自动化流程，该流程利用雅可比矩阵导出的特征生成多样化的机器人模型和丰富的轨迹数据。数据集包含8,192个具有不同惯性和摩擦特性的机器人。通过利用注意力机制，我们的模型有效地捕捉了时间和空间上的依赖关系。实验结果突显了序列长度、采样率和架构的影响，其中最佳配置（序列长度64、64 Hz、四层、32个头）实现了0.8633的验证R2分数。质量和惯性估计接近完美精度，库仑摩擦达到中高精度，而粘性摩擦和远端连杆质心估计仍更具挑战性。这些结果表明，将Transformer与自动化数据集生成和运动学特征增强相结合，能够实现可扩展且精确的动态参数估计，有助于提升机器人系统的仿真到现实迁移能力。