In a collaborative environment where multiple robots and human beings are expected

to collaborate to perform a task, it becomes essential for a robot to be aware of multiple

agents working in its work environment. A robot must also learn to adapt to

different agents in the workspace and conduct its interaction based on the presence

of these agents. A theoretical framework was introduced which performs interaction

learning from demonstrations in a two-agent work environment, and it is called

Interaction Primitives.

This document is an in-depth description of the new state of the art Python

Framework for Interaction Primitives between two agents in a single as well as multiple

task work environment and extension of the original framework in a work environment

with multiple agents doing a single task. The original theory of Interaction

Primitives has been extended to create a framework which will capture correlation

between more than two agents while performing a single task. The new state of the

art Python framework is an intuitive, generic, easy to install and easy to use python

library which can be applied to use the Interaction Primitives framework in a work

environment. This library was tested in simulated environments and controlled laboratory

environment. The results and benchmarks of this library are available in the

related sections of this document.