Emergence of exploratory look-around behaviors through active observation completion

A journal version of this work in conjunction with our prior work on Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks has been published in Science Robotics 2019.

Emergence of exploratory look-around behaviors through active observation completion
Santhosh K. Ramakrishnan, Dinesh Jayaraman, Kristen Grauman
Science Robotics 2019

A cleaned version of this codebase along with new transfer tasks are available at https://github.com/srama2512/visual-exploration.

Sidekick Policy Learning

This repository contains code and data for the paper

Sidekick Policy Learning for Active Visual Exploration
Santhosh K. Ramakrishnan, Kristen Grauman
ECCV 2018

Setup

First install anaconda and setup a new environment. Install anaconda from: https://www.anaconda.com/download/

conda create -n spl python=2.7
source activate spl

Clone this project repository and setup requirements using pip.

git clone https://github.com/srama2512/sidekicks.git
cd sidekicks
pip install -r requirements.txt

Download preprocessed SUN360 and ModelNet data.

wget http://vision.cs.utexas.edu/projects/sidekicks/data.zip
unzip data.zip

Sidekick scores for ours-rew, ours-demo, rnd-rewards on both datasets have been provided here. The one-view model used to generate them have also been provided.

Evaluating pre-trained models

All the pre-trained models have been provided here. To evaluate them, download them to the models directory. To reproduce results from the paper:

wget http://vision.cs.utexas.edu/projects/sidekicks/models.zip
unzip models.zip
sh evaluation_script_final.sh

Evaluation examples

Evaluating SUN360 one-view baseline on the test data with avg metric:

python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
				  --model_path models/sun360/one-view.net --T 1 --M 8 --N 4 \
				  --start_view 2 --save_path dummy/

Evaluating SUN360 ltla baseline on the test data with avg metric:

python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
				  --model_path models/sun360/ltla.net --T 4 --M 8 --N 4 \
				  --start_view 2 --save_path dummy/

Evaluating SUN360 ltla baseline on the test data with adv metric:

python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
				  --model_path models/sun360/ltla.net --T 4 --M 8 --N 4 \
				  --start_view 2 --save_path dummy/

Evaluating SUN360 rnd-actions baseline on test data with avg metric:

python eval.py --h5_path data/sun360/sun360_processed.h5 --dataset 0 \
				  --model_path models/sun360/rnd-actions.net --T 4 --M 8 --N 4 \
				  --start_view 2 --actorType random --save_path dummy/

Evaluating ModelNet Hard one-view baseline on test (seen and unseen) data with avg metric:

python eval.py --h5_path modelnet30_processed.h5 \
				  --h5_path_unseen modelnet10_processed.h5 --dataset 1 \
				  --model_path models/modelnet_hard/one-view.net --T 1 --M 9 --N 5 \
				  --start_view 2 --save_path dummy/

Training models

Ensure that the pre-trained models and pre-computed scores are downloaded and extracted.

Training one-view model on SUN360 with default settings:

python main.py --T 1 --training_setting 0 --epochs 100 \
				  --save_path saved_models/sun360/one-view

Training ltla baseline on SUN360 with default settings (starting from pre-trained one-view model):

python main.py --T 4 --training_setting 1 --epochs 1000 \
				  --save_path saved_models/sun360/ltla/  \
				  --load_model models/sun360/one-view.net

Training ours-rew on SUN360 with default settings (with pre-computed score):

python main.py --T 4 --training_setting 1 --epochs 1000 \
				  --save_path saved_models/sun360/ours-rew/ \
				  --load_model models/sun360/one-view.net --expert_rewards True \
				  --rewards_h5_path scores/sun360/ours-rew-scores.h5

Training ours-demo on SUN360 with default settings (with pre-computed score):

python main.py --T 4 --training_setting 1 --epochs 1000 \
				  --save_path saved_models/sun360/ours-demo/ \
				  --load_model models/sun360/one-view.net --expert_trajectories True \
				  --utility_h5_path scores/sun360/ours-demo-scores.h5

Training ltla baseline on ModelNet Hard with default settings (starting from pre-trained one-view model):

python main.py --h5_path data/modelnet_hard/modelnet30_processed.h5 \
				  --training_setting 1 --dataset 1 --T 4 --M 9 --N 5 \
				  --load_model models/modelnet_hard/one-view.net \
				  --save_path saved_models/modelnet_hard/ltla/

The other ModelNet Hard models can be trained similar to SUN360 models. To train actor critic models, set --baselineType critic. To add full observability to the critic (for asymm-ac), set --critic_full_obs True.

Visualization

From the repository directory, start jupyter notebook and open visualize_policy_paper.ipynb. Perform the TODOs mentioned in the comments (setting the correct paths) and run the entire script. It will generate tensorboard files contained visualized heatmaps on several examples.

srama2512 / sidekicks