Imitation Learning for Flexible Object Grasping Using Stiffness Information

Recently, imitation learning, which reproduces human actions, has attracted attention. In this study, we aim to realize a robot capable of grasping flexible objects using imitation learning. Specifically, two types of plastic bottles are prepared, and at the moment the robot grasps a plastic bottle, it discriminates between the two and reproduces human force. In this paper, we propose a method to derive stiffness from the angle and torque of the robot's end-effector, and to simultaneously learn the angle, angular velocity, torque, and stiffness using LSTM (Long Short-Term Memory). This method enables the robot to take into account the stiffness of the object at the moment of contact and to reproduce delicate human motions with higher accuracy.