While neural density fields offer significant advantages for robot self-modeling, there are some potential limitations that need to be addressed:
Complexity of Training Data: Generating annotated training data for neural density fields can be labor-intensive and require a large amount of data. Addressing this limitation involves developing efficient data generation strategies, such as curriculum learning, to optimize the training process.
Generalization to Unseen Configurations: Neural density fields may struggle to generalize to unseen configurations or extreme poses of the robot. To address this, techniques like data augmentation, regularization, and transfer learning can be employed to improve the model's ability to generalize.
Computational Complexity: Training and inference with neural density fields can be computationally intensive, especially for high-DOF robots. Utilizing hardware acceleration, parallel processing, and optimization techniques can help mitigate this limitation and improve the efficiency of the model.