Many robots utilize commercial force/torque sensors to identify physical properties of a priori unknown objects. However, such sensors can be difficult to apply to smaller-sized robots due to their weight, size, and high-cost. In this paper, we propose a framework for smaller-sized humanoid robots to estimate the inertial properties of unknown objects without using force/torque sensors. In our framework, a neural network is designed and trained to predict joint torque outputs. The neural network's inputs are robot's joint angle, steady-state joint error, and motor current. All of these can be easily obtained from many existing smaller-sized robots. As the joint rotation direction is taken into account, the neural network can be trained with a smaller sample size, but still maintains accurate torque estimation capability. Eventually, the inertial properties of the objects are identified using a nonlinear optimization method. Our proposed framework has been demonstrated on a NAO humanoid robot.

翻译：许多机器人使用商业力/感应器来识别先天未知物体的物理特性。 但是, 由于其重量、 大小和高成本, 这种感应器可能难以适用于规模较小的机器人。 在本文中, 我们提议了一个规模较小的人类机器人框架, 用于估算未知物体的惯性特性, 而不使用强力/ 感应器。 在我们的框架中, 设计并培训了一个神经网络, 以预测共同的硬性输出。 神经网络的输入是机器人的共同角度、 稳态联合错误和运动流。 所有这些都可以很容易地从许多现有的较小机器人那里获得。 由于考虑到联合旋转方向, 神经网络可以使用较小样本大小来培训, 但仍然保持精确的原子估计能力。 最终, 使用非线性优化方法确定了这些物体的惯性特性 。 我们提议的框架已经在NAO 类机器人上演示了 。