Peg-in-hole assembly skill imitation learning method based on ProMPs under task geometric representation - Zang, Yajing; Wang, Pengfei; Zha, Fusheng; Guo, Wei; Zheng, Chao; Sun, Lining (Frontiers in Neurorobotics, 2023)
Behavioral Cloning (BC) is a common imitation learning method which utilizes neural networks to approximate the demonstration action samples for task manipulation skill learning. However, in the real world, the demonstration trajectories from human are often sparse and imperfect, wihch makes it challenging to comprehensively learn directly from the demonstration action samples. Therefore, in this paper, we proposes a streamlined imitation learning method under the terse geometric representation to take good advantage of the demonstration data, and then realize the manipulation skill learning of assembly tasks. Firstly, We map the demonstration trajectories into the geometric feature space. Then we align the demonstration trajectories by Dynamic Time Warping (DTW) method to get the unified data sequence so we can segment them into several time stages. The Probability Movement Primitives (ProMPs) of the demonstration trajectories are then extracted, so we can generate a lot of task trajectories to be the global strategy action samples for training the neural networks. Notalby, we regard the current state of the assembly task as the via point of the ProMPs model to get the generated trajectories, while the time point of the via point is calculated according to the probability model of the different time stages. And we get the action of the current state according to the target position of the next time state. Finally, we train the neural network to obtain the global assembly strategy by Behavioral Cloning. We conducted experiments in a simulation environment based on Pybullet + Gym, And it is proved that the proposed method achieves higher success rates compared to traditional imitation learning methods while exhibiting reasonable generalization capabilities. We also verified the feasibility of the learned assembly strategy on a real robotic platform.