To extend this approach to handle more complex robot morphologies, such as multi-limb or modular robots, several modifications and enhancements can be implemented:
Hierarchical Neural Fields: Implementing hierarchical neural fields can help capture the interdependencies and interactions between different limbs or modules of the robot. Each hierarchical level can represent a different aspect of the robot's morphology, allowing for a more detailed and comprehensive self-model.
Dynamic DOF Allocation: Introducing a dynamic allocation of degrees of freedom (DOFs) based on the specific configuration of the robot can enable more flexibility in modeling complex morphologies. This adaptive approach can adjust the neural density field representation based on the current state of the robot.
Multi-View Integration: Incorporating information from multiple camera views can provide a more comprehensive understanding of the robot's morphology, especially in the case of multi-limb or modular robots. By fusing data from different perspectives, the neural density field can capture the full complexity of the robot's structure.