Imitation Learning Based Manipulator Skill Training for Home Environment

This study implements a vision-driven imitation learning method for predicting robots' action sequences. By employing a U-Net with conditional feature modulation and diffusion models, it effectively learns multi-modal actions from human experts' demonstrations, ensuring the stability and quality of action outputs. Furthermore, we introduced an adaptive sampling strategy that dynamically adjusts the sampling steps in the reverse process of the diffusion model based on distance, which can enhance the real-time performance of the model. Experiments show that this method reduces the time required for inference while maintaining the success rate of robotic tasks, thus providing an efficient solution for the learning and training of manipulators in home environments.