We present a fast learning-based inertial parameters estimation framework capable of understanding the dynamics of an unknown object to enable a humanoid (or manipulator) to more safely and accurately interact with its surrounding environments. Unlike most relevant literature, our framework doesn't require to use of a force/torque sensor, vision system, and a long-horizon trajectory. To achieve fast inertia parameter estimation, a time-series data-driven regression model is utilized rather than solving a constrained optimization problem. Due to the challenge of obtaining a large number of the ground truth of inertia parameters in the real world, we acquire a reliable dataset in a high-fidelity simulation that is developed using a real-to-sim adaptation. The adaptation method we introduced consists of two components: 1) \textit{Robot System Identification} and 2) \textit{Gaussian Processes}. We demonstrate our method with a 4-DOF single manipulator of a wheeled humanoid robot, SATYRR. Results show that our method can identify the inertial parameters of various unknown objects quickly while maintaining sufficient accuracy compared to other methods. Manipulation and locomotion experiments were also carried out to show the benefit of using the estimated inertia parameters from control perspective.

翻译：我们提出了一种基于学习的快速惯性参数估计框架，能够理解未知物体的动力学特性，使人形机器人（或机械臂）更安全、更精确地与周围环境交互。与现有大部分文献不同，本框架无需使用力/力矩传感器、视觉系统及长时域轨迹。为实现快速惯性参数估计，采用时间序列数据驱动回归模型，而非求解约束优化问题。针对真实世界难以获取大量惯性参数真实值的问题，我们通过真实到仿真自适应构建高保真仿真环境，并获取可靠数据集。所提出的自适应方法包含两个部分：1) 机器人系统辨识，2) 高斯过程。我们以轮式人形机器人SATYRR的四自由度单机械臂为载体进行实验验证。结果表明，相较于其他方法，本方法能在保持足够精度的前提下快速辨识各类未知物体的惯性参数。我们还进行了操作与 locomotion 实验，从控制角度验证了使用估计惯性参数的优越性。