pyrobolearn/pyrobolearn/models

Learning models

In this folder, we provide the various learning models. These can be categorized into two categories: movement primitives and general function approximators.

These include:

• Central pattern generators (CPG; the version provided by )
• Dynamic movement primitives (DMP)
• Probabilistic movement primitives (ProMP)
• Kernelized movement primitives (KMP)
• Linear models
• PCA models
• Polynomial models
• Gaussian mixture models (GMM) with its regression counterpart (GMR)
• Gaussian processes (GP; it uses/wraps the GPyTorch library)
• Neural networks (currently, only MLP are provided)

TODO:

• finish to implement the models
• provide multiple tests/examples for each model
• implement other models such as HMMs

what to check/look next?

Check the approximators, policies, values, and dynamics folders.