Accurate inertial parameter identification is crucial for the simulation and control of robots encountering intermittent contact with the environment. Classically, robots' inertial parameters are obtained from CAD models that are not precise (and sometimes not available, e.g., Spot from Boston Dynamics), hence requiring identification. To do that, existing methods require access to contact force measurement, a modality not present in modern quadruped and humanoid robots. This paper presents an alternative technique that utilizes joint current/torque measurements -- a standard sensing modality in modern robots -- to identify inertial parameters without requiring direct contact force measurements. By projecting the whole-body dynamics into the null space of contact constraints, we eliminate the dependency on contact forces and reformulate the identification problem as a linear matrix inequality that can handle physical and geometrical constraints. We compare our proposed method against a common black-box identification mrethod using a deep neural network and show that incorporating physical consistency significantly improves the sample efficiency and generalizability of the model. Finally, we validate our method on the Spot quadruped robot across various locomotion tasks, showcasing its accuracy and generalizability in real-world scenarios over different gaits.

翻译：精确的惯性参数辨识对于机器人在与环境发生间歇性接触时的仿真与控制至关重要。传统上，机器人的惯性参数通过CAD模型获取，但这些模型往往不够精确（有时甚至无法获取，例如波士顿动力的Spot机器人），因此需要进行参数辨识。现有方法通常需要接触力测量数据，而这一测量模态在现代四足机器人和人形机器人中并不具备。本文提出了一种替代技术，利用关节电流/力矩测量——现代机器人中的标准传感模态——来辨识惯性参数，无需直接测量接触力。通过将全身动力学投影至接触约束的零空间，我们消除了对接触力的依赖，并将辨识问题重新表述为能够处理物理与几何约束的线性矩阵不等式。我们将所提出的方法与一种基于深度神经网络的常见黑箱辨识方法进行比较，结果表明，引入物理一致性显著提升了模型的样本效率与泛化能力。最后，我们在Spot四足机器人上通过多种运动任务验证了本方法，展示了其在不同步态下真实场景中的准确性与泛化性能。