Baxter learns to lie a knot

Getting a robot to learn based on its interactions with environment is one of the most powerful ideas of all. Most of our object manipulation revolves around the rigid body assumption. But in this project, I implemented a work that enables the robot to learn to tie a knot purely based on interacting with its environment. This a self supervised deep learning technique applied to the reinforcement learning problem of learning an effective policy.w. This repository contains codes for an experiment, where Baxter learns to lie a knot. This series of work was based on the principle of computational sensorimotor learning actively followed by Prof. Jitendra Malik's group at UC, Berkely. More details about the work can be found in this link. More details about my implementation can be found at my portfolio

Library requirements

The library requirements are listed in each separate subdirectory

Directory structure.

1. baxter_ros - A baxter package that executes actions using Moveit package
2. goal_recogniser - Code and infrastructure for training a goal recogniser network
3. inference - Code for running inference using trained models
4. inverse_model - Code for training the joint model
5. kinect_ros - launch files for launching kinect cameras.