用Transformer和自动数据生成，精准估算机械臂动力学参数。
Data-Driven Dynamic Parameter Learning of manipulator robots
- 基于Transformer捕捉长时序与空间依赖关系
- 最佳配置下质量与惯性估计误差极低，摩擦参数中等偏上
- 适合需要高精度仿真-现实迁移的机器人系统研究者
弥合仿真到现实的差距仍是机器人领域的核心挑战，准确的动力学参数估计对基于模型的控制、真实仿真及安全部署至关重要。传统解析方法在复杂结构与交互下表现不足，数据驱动方法虽具潜力，但常规神经网络难以捕捉长期依赖。本研究提出一种基于Transformer的动力学参数估计方法，并构建自动化流水线，利用雅可比导出特征生成多样化的机器人模型与丰富轨迹数据，涵盖8192个具有不同惯性与摩擦特性的机器人。借助注意力机制，模型有效建模时空依赖。实验表明序列长度、采样率与架构影响显著，最优配置（序列长度64，64 Hz，四层，32头）验证集R²达0.8633。质量与惯性估计近乎完美，库仑摩擦中高精度，而粘性摩擦与远端连杆质心仍具挑战。结果表明，结合Transformer、自动化数据生成与运动学增强，可实现可扩展、高精度的动力学参数估计，提升机器人系统的仿真-现实迁移能力。
原文摘要 · Abstract (English)
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
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