RECVIS-transporter-networks

This repository is my work on the course Object Recognition and Computer Vision given at the MVA by Jean Ponce, Ivan Laptev, Cordelia Schmid and Josef Sivic.

Objective

Transporter Networks

Firstly, we use the Transporter Networks published in this article. This new type of network aims at achieving state-of-the-art performances on robotic manipulation tasks. This idea is to decompose a robotic manipulation in 2 steps:

• a pick step where the robot picks an object. Transporter Networks uses an equivariant attention network based on Resnet for such steps.
• a place step conditionned by the pick step where the robot puts down the picked object. Transporter networks uses an action-value function invariant to the pick step. Thanks to the 2 equivariant and invariant properties, the transporter networks are highly sample-efficient.

The authors have published ravens: a python framework to simulate 10 robot manipulation tasks. In this work, we will focus on 2 tasks:

• block-insertion: the robot needs to pick a L-shaped object and put it on a L-shaped support
• manipulating-rope: the robot needs to manipulate a rope so that it finishes the incomplete perimeter of a square.

Depth estimation of the transporter network inputs

Transport networks uses RGB-Depth images. While obtaining depth images is getting easier, it is still preferable to only use RGB images. We use the BinsFormer framework published in this article. This algorithm predicts depth on RGB images.

The authors have published the algorithm inside this toolbox.

Installation

The repository uses 2 different packages:

• ravens: a gym-like framework which implements transporter networks. It uses tensorflow==2.3, which works best wih cuda-10.1. This version of cuda is going to be the primary one.
• Monocular-Depth-Estimation-Toolbox: a toolbox which implements Adabins and BinsFormer. It uses torch==1.8, which works best/only with cuda-10.2 This version of cuda is going to be the secondary one.

In the installation process, you will need to:

• install the nvidia drivers
• install cudnn7, used for cuda-10.1
• install 2 versions of cuda. Check this link
  • install cuda-10.1
  • install cuda-10.2
  • update your env variable $LD_LIBRARY_PATH
  • update your symlink at /usr/local/cuda so that it points towards /usr/local/cuda-10.1

Installing cuda

Basic script to install cuda-10.2. Check this link for more information. You also need to install cuda-10.1.

# Install cuda-10.2
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
sudo mv cuda-ubuntu1804.pin /etc/apt/preferences.d/cuda-repository-pin-600
wget https://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
sudo dpkg -i cuda-repo-ubuntu1804-10-2-local-10.2.89-440.33.01_1.0-1_amd64.deb
sudo apt-key add /var/cuda-repo-10-2-local-10.2.89-440.33.01/7fa2af80.pub
sudo apt-get update
sudo apt-get -y install cuda-10-2

# Add symlink to cuda-10.1
cd /usr/local
sudo rm cuda
ln -s cuda-10.1 cuda

Installation of the packages

Then you can simply install the repository and its submodules

# Clone the repository 
git clone --recurse-submodules [email protected]:MatiasEtcheve/RECVIS-transporter-networks.git

# install the project requirements
pip install -r requirements.txt

# install ravens
pip install -r ravens/requirements.txt
pip install -e ravens/ # editable version

# install the toolbox for depth estimation
pip install -e Monocular-Depth-Estimation-Toolbox/ # editable version
mim install mmcv-full==1.4.0

Download the pretrained model checkpoint

The repository uses BinsFormer pretrained model. You need to save it (in checkpoints/ for instance.)

mkdir checkpoints && cd checkpoints
gdown https://drive.google.com/uc\?id\=1tcWx_BQBNJHpP5-RUWGWjpVRfeUiUMzJ