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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 ablation study

Transport networks uses RGB-Depth images. While obtaining depth images is getting easier, it is still preferable to only use RGB images.

We develop 2 ways of deleting depth information.

Soft deletion

Here, we only delete the depth information, right before it is given to the transporter network, after the top down reconstruction.

Hard deletion

Here, we directly estimate the depth from RGB images, before the top down reconstruction. We use the AdaBins framework published in this article, to predict depth on RGB images.

The authors have published the algorithm inside this repository.

Note: here, I made a fork of Ravens in order to add a command-line for hard-ablation and adding a few saved information when testing a trained transporer network on a test set.

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.
  • AdaBins: a repository which implements Adabins. 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

Install Pytorch3d

curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
tar xzf 1.10.0.tar.gz
export CUB_HOME=$PWD/cub-1.10.0
pip install "git+https://github.com/facebookresearch/pytorch3d.git"

Installation of the packages

Then you can simply install the repository and its submodules

# Clone the repository 
git clone --recurse-submodules git@github.com: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

In order to install AdaBins you will need to create a setup file:

echo -e 'from setuptools import find_packages, setup

setup(
    name="adabins",
    version="0.0.1",
    packages=find_packages(),
    python_requires=">=3.6",
)' >> AdaBins/setup.py

mkdir AdaBins/adabins
mv -v AdaBins/* -t AdaBins/adabins
mv -t AdaBins AdaBins/adabins/setup.py AdaBins/adabins/README.md AdaBins/adabins/LICENSE

And then install it, so you can directly run from adabins import ...:

pip install -e AdaBins/ # editable version

Download the data

Download the dataset

You can directly download the dataset from Ravens. Here is how to fetch them:

mkdir dataset
wget https://storage.googleapis.com/ravens-assets/block-insertion.zip -P dataset/
unzip dataset/block-insertion
wget https://storage.googleapis.com/ravens-assets/manipulating-rope.zip -P dataset/
unzip dataset/manipulating-rope

Download the pretrained models for AdaBins

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

mkdir AdaBins/adabins/pretrained
gdown "1HMgff-FV6qw1L0ywQZJ7ECa9VPq1bIoj&confirm=t" -O AdaBins/adabins/pretrained/ # download kitty model
gdown "1lvyZZbC9NLcS8a__YPcUP7rDiIpbRpoF&confirm=t" -O AdaBins/adabins/pretrained/ # download nyu model

Note: if you have an error like Cannot retrieve the public link of the file, check this thread.

Working tree

After the installation process, you should have something looking like:

.
├── AdaBins/ # AdaBins Framework
│   ├── LICENSE
│   ├── README.md
│   ├── adabins/
│   │   ├── pretrained/ # Contain Adabins models
│   │   │   ├── adabins_efnetb5_kitti.pth
│   │   │   └── adabins_efnetb5_nyu.pth
│   │   ├── ...
│   └── setup.py
├── dataset/ # Dataset for the 2 simulated tasks
│   ├── block-insertion-test/
│   │   ├── action/
│   │   ├── color/
│   │   ├── depth/
│   │   ├── info/
│   │   └── reward/
│   ├── block-insertion-train/
│   │   ├── ...
│   ├── manipulating-rope-test/
│   │   ├── ...
│   └── manipulating-rope-train/
│       ├── ...
├── ravens/
│   ├── setup.py
│   └── ...
├── RGB/
│   ├── logs/
│   └── predictions/
├── RGB-Depth/
│   ├── checkpoints/
│   ├── logs/
│   └── predictions/
├── RGB-Estimated Depth/
│   ├── logs/
│   └── predictions/
├── agent_visualisation.ipynb # visualize what an agent does with `pybullet`
├── requirements.txt # requirements of the project. Maybe not exact
└── training_visualisation.ipynb # plots curves

Each RGB-Depth, RGB and RGB-Estimated Depth folders contain the results of the original transporter networks and the 2 ablation studies: soft and hard deletion.

They each contains the logs of the training and the predictions ran on the test sets of each tasks.

Results

With this repository, I was able to reproduce the results stated by the transport network authors. Specifically, here are some observed policies.

On block-insertion task:

Before training After training
Slowest success Typical fail

On manipulating-rope task:

Before training After training
Fastest success Slowest success Typical fail