This project is a part of a study conducted by ARL STRONG.

In this repository, the code associated with the paper: "Segment, Compare and Learn: Creating Movement Libraries of Complex Task for Learning from Demonstration" is presented.

Personal website

Discrete, Gaussian, and Heterogenous HMM models full implemented in Python. Missing data, Model Selection Criteria (AIC/BIC), and Semi-Supervised training supported. Easily extendable with other ty…

A library for programmatically generating equivariant layers through constraint solving

Course website for 6.S184/6.S975: Generative AI with Stochastic Differential Equations

Bayesian learning and inference for state space models

This repository contains a reading list of papers on Time Series Segmentation. This repository is still being continuously improved.

O(N) framework for Multivariate time series segmentation using sparse graph recovery model uGLAD

Dynamical movement primitives (DMPs), probabilistic movement primitives (ProMPs), and spatially coupled bimanual DMPs for imitation learning.

RISE: "Live" Reveal.js JupyterLab Slideshow Extension

A template gymnasium environment for users to build upon

A modern resume template written in Typst, inspired by Awesome-CV

Oscar documentation

A standard API for reinforcement learning and a diverse set of reference environments (formerly Gym)

NMA deep learning course