- - CSE571 Project
CSE571 Project
- -

CSE571 Project

Team STAB

Team members

  • Keshav Rajasekaran
  • James Goin
  • Yue Zhang

Project description

Our plan is to learn a simple navigation system for a turtlebot. Using the Dagger learning algorithm, we hope to be able to learn a model that allows the robot to navigate a hallway and enter a door, using Kinect sensory data. The Dagger algorithm is for imitation learning in which the controls of an expert (a human, in this case) is used to train a policy for a robot.

Link to Github repository

Goals/schedule

Weekly Progress

Midterm goals

We hope to have implemented the Dagger learning algorithm and have it working in simulation for various scenarios.

  1. Implement the Dagger algorithm in simple simulated scenarios. One simple scenario is a scenario where the goal is to navigate a circular path through a track. Another simple scenario is for one robot to pursue another robot.
  2. Start working with the turtlebot, and understand how to control it and how to save data from its sensors. Finish the basic Turtlebot tutorials.
  3. Save a sequence of data from the Turtlebot’s laser scanners for input as the state into Dagger.
  4. Create a framework that can be used to train Dagger for the Turtlebot - saves images and human control inputs, and either sends human or learned controls to the robot.

End goal

We have trained the robot so that it is capable of autonomously navigating through a hallway and pass through an open doorway without hitting obstacles.

  1. Create a program that can send movement commands (given depth images) to the Turtlebot.
  2. Train a policy using Dagger for the Turtlebot.

Simulation results

We trained the simulated red robot to pursue the white robot. A video of the trained model can be found here.

Gazebo results

The Gazebo simulation provides the entire API for the Turtlebot, with simulated controls and sensory data. We trained Dagger for a few rounds to avoid obstacles. Video

Robot results

We trained the Turtlebot using Dagger to navigate hallways using Hokuyo laser depth scan data. A video of the training process and the trained model can be found here.

Division of labor

All of us are working with the turtlebot, and we are all learning how to run controls on it.

Robot sensing & control, simulation: Keshav

Robot control, Gazebo turtlebot simulation: James

Dagger implementation, simulation: Yue