Baseline models for the Object Navigation task in the RoboTHOR and iTHOR environments

This project contains the code for training baseline models on the PointNav task. In this setting the agent spawns at a location in an environment and is tasked to explore the environment until it finds an object of a certain type (such as TV or Basketball). Once the agent is confident that it has the object within sight it executes the END action which terminates the episode. If the agent is within a set distance to the target (in our case 1.5 meters) and the target is visible within its observation frame the agent succeeded, else it failed.

Provided are experiment configs for training a simple convolutional model with an GRU using RGB, Depth or RGBD as inputs in RoboTHOR and iTHOR.

The experiments are set up to train models using the DD-PPO Reinforcement Learning Algorithm.

To train an experiment run the following command from the allenact root directory:

python main.py -o <PATH_TO_OUTPUT> -c -b <BASE_DIRECTORY_OF_YOUR_EXPERIMENT> <EXPERIMENT_NAME>

Where <PATH_TO_OUTPUT> is the path of the directory where we want the model weights and logs to be stored, <BASE_DIRECTORY_OF_YOUR_EXPERIMENT> is the directory where our experiment file is located and <EXPERIMENT_NAME> is the name of the python module containing the experiment. An example usage of this command would be:

python main.py -o storage/objectnav-robothor-rgb -b projects/objectnav_baselines/experiments/robothor/ objectnav_robothor_rgb_resnet_ddppo

This trains a simple convolutional neural network with a GRU using RGB input passed through a pretrained ResNet-18 visual encoder on the PointNav task in the RoboTHOR environment and stores the model weights and logs to storage/pointnav-robothor-rgb. hings you can run with bash commands