Many robots utilize commercial force/torque sensors to identify inertial properties of unknown objects. However, such sensors can be difficult to apply to small-sized robots due to their weight, size, and cost. In this paper, we propose a learning-based approach for estimating the mass and center of mass (COM) of unknown objects without using force/torque sensors at the end-effector or on the joints. In our method, a robot arm carries an unknown object as it moves through multiple discrete configurations. Measurements are collected when the robot reaches each discrete configuration and stops. A neural network is designed to estimate joint torques from encoder discrepancies. Given multiple samples, we derive the closed-form relation between joint torques and the object's inertial properties. Based on the derivation, the mass and COM of object are identified by weighted least squares. In order to improve the accuracy of inferred inertial properties, an attention model is designed to generate weights of joints, which indicate the relative importance for each joint. Our framework requires only encoder measurements without using any force/torque sensors, but still maintains accurate estimation capability. The proposed approach has been demonstrated on a 4 degree of freedom (DOF) robot arm.

翻译：许多机器人利用商用力/扭矩传感器来识别未知物体的惯性属性。然而，由于重量、尺寸和成本等因素，此类传感器难以应用于小型机器人。本文提出了一种基于学习的方法，可在不使用末端执行器或关节处力/扭矩传感器的情况下，估计未知物体的质量和质心。在我们的方法中，机械臂携带未知物体移动至多个离散构型，并在到达每个离散构型并停止时进行测量。我们设计了一个神经网络，通过编码器差异来估计关节扭矩。基于多个样本，推导出关节扭矩与物体惯性属性之间的闭式关系。基于该推导，采用加权最小二乘法识别物体的质量和质心。为提高推断惯性属性的准确性，设计了一个注意力模型以生成关节权重，用于指示每个关节的相对重要性。我们的框架仅需编码器测量值，无需使用任何力/扭矩传感器，但仍能保持准确的估计能力。该方法已在四自由度机械臂上得到验证。