lstm_explore

"Visual Episodic Memory based Exploration". A convolutional LSTM autoencoder curiosity method for robot exploration.

This work explores the use of visual episodic memory as a source of intrinsic motivation for robotic exploration problems. Using a convolutional recurrent neural network autoencoder, the agent learns an efficient representation for spatiotemporal features such that accurate sequence prediction can only happen once spatiotemporal fea- tures have been learned. Structural similarity between ground truth and autoencoder generated images is used as an intrinsic motivation signal to guide exploration. Our proposed episodic memory model also implicitly accounts for the agent’s actions, motivating the robot to seek new interactive experiences rather than just areas that are visually dissimilar. When guiding robotic exploration, our proposed method outperforms the Curiosity-driven Variational Autoencoder (CVAE) at finding dynamic anomalies.

Getting Started

In addition to the requirements.txt, this experiment requires ROS(Noetic) and Gazebo installation. This configuration was tested on Ubuntu 20.04 with two Nvidia GPU's and CUDA 11.2. The ROS_files directory contains the Gazebo map as well as Turtlebot configuration files. lstmAE.py is the primary convolutional LSTM exploration method with vae.py being the variational autoencoder method for comparison.

Install ROS Noetic with Gazebo

https://wiki.ros.org/noetic/Installation/Ubuntu

Install dependencies

$ pip install -r requirements.txt

ROS commands

roslaunch turtlebot3_gazebo turtlebot3_curiosity.launch
roslaunch turtlebot3_slam turtlebot3_slam.launch
roslaunch turtlebot3_navigation move_base.launch
roslaunch explore_lite explore.launch

Then run python script

python3 lstmAE.py