To extend the proposed trajectory augmentation method for handling more complex transformations, such as leveraging deep learning-based models with suitable loss functions, we can explore the integration of advanced techniques like generative adversarial networks (GANs) or variational autoencoders (VAEs). By incorporating GANs, we can introduce a generator network that learns to generate realistic trajectories while a discriminator network evaluates the authenticity of the generated trajectories compared to the expert data. This adversarial training process can help in creating more diverse and realistic trajectories that align closely with the original expert data. Additionally, VAEs can be utilized to learn a latent space representation of trajectories, enabling the generation of new trajectories by sampling from this learned distribution. By optimizing the VAE with a suitable loss function that captures the similarity between the generated trajectories and the expert data, we can ensure that the augmented trajectories maintain the essential characteristics of the original data.