While the Conditional Flow Matching (CFM) formulation offers several advantages over traditional diffusion models, such as greater flexibility and efficiency, it also presents certain limitations. One potential limitation is the reliance on the quality of the learned vector field, which can lead to suboptimal performance if the model fails to accurately capture the underlying data distribution. This can be particularly problematic in high-dimensional spaces, where the complexity of the action distribution increases.
To address this limitation, one approach is to enhance the training process by incorporating more diverse and representative expert demonstrations. This can be achieved through data augmentation techniques, such as adding noise or perturbations to the training data, which can help the model generalize better to unseen scenarios. Additionally, employing ensemble methods or multi-model approaches can improve robustness by aggregating predictions from multiple CFM models, thereby reducing the impact of any single model's shortcomings.
Another limitation is the potential difficulty in optimizing the CFM formulation for certain types of data distributions, particularly those with high multimodality. To mitigate this, researchers can explore hybrid models that combine CFM with other generative techniques, such as variational autoencoders (VAEs) or generative adversarial networks (GANs), to better capture complex distributions. By leveraging the strengths of multiple generative frameworks, the overall performance of robotic manipulation policies can be significantly improved.