davidscg / arm

Q-attention (within the ARM system) and coarse-to-fine Q-attention (within C2F-ARM system).

Attention-driven Robotic Manipulation (ARM)

Codebase of Q-attention, coarse-to-fine Q-attention, and other variants. Code from the following papers:

• Q-attention: Enabling Efficient Learning for Vision-based Robotic Manipulation (ARM system)
• Coarse-to-Fine Q-attention: Efficient Learning for Visual Robotic Manipulation via Discretisation (C2F-ARM system)
• Coarse-to-Fine Q-attention with Learned Path Ranking (C2F-ARM+LPR system)
• Coarse-to-Fine Q-attention with Tree Expansion

Installation

ARM is trained using the YARR framework and evaluated on RLBench 1.1.0.

Install all of the project requirements:

# Create conda environment
conda create -n arm python=3.8

# Install PyTorch 2.0. Go to PyTorch website to install other versions.
conda install pytorch=2.0 torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia

# Install YARR
pip install git+https://github.com/stepjam/YARR.git

# Install CoppeliaSim 4.1.0 for Ubuntu 20.04
# Refer to PyRep README for other versions
export COPPELIASIM_ROOT=${HOME}/.local/bin/CoppeliaSim
curl -O https://www.coppeliarobotics.com/files/CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz
mkdir -p $COPPELIASIM_ROOT && tar -xf CoppeliaSim_Edu_V4_1_0_Ubuntu20_04.tar.xz -C $COPPELIASIM_ROOT --strip-components 1
## Add environment variables into bashrc (or zshrc)
echo "export COPPELIASIM_ROOT=$COPPELIASIM_ROOT
export LD_LIBRARY_PATH=\$LD_LIBRARY_PATH:\$COPPELIASIM_ROOT
export QT_QPA_PLATFORM_PLUGIN_PATH=\$COPPELIASIM_ROOT" >> ~/.bashrc

# Install PyRep
git clone https://github.com/stepjam/PyRep.git .local/PyRep
cd .local/PyRep
pip install -r requirements.txt
pip install .
cd ../..

# Install RLBench
git clone https://github.com/stepjam/RLBench.git .local/RLBench
cd .local/RLBench
pip install -r requirements.txt
pip install .
cd ../..

# Install ARM dependencies
pip install -r requirements.txt

Running experiments

Be sure to have RLBench demos saved on your machine before proceeding. To generate demos for a task, go to the tools directory in RLBench (rlbench/tools), and run:

python dataset_generator.py --save_path=/mnt/my/save/dir --tasks=take_lid_off_saucepan --image_size=128,128 \
--renderer=opengl --episodes_per_task=100 --variations=1 --processes=1

Experiments are launched via Hydra. To start training C2F-ARM on the take_lid_off_saucepan task with the default parameters on gpu 0:

python launch.py method=C2FARM rlbench.task=take_lid_off_saucepan rlbench.demo_path=/mnt/my/save/dir framework.gpu=0

To launch C2F-ARM+LPR:

python launch.py method=LPR rlbench.task=take_lid_off_saucepan rlbench.demo_path=/mnt/my/save/dir framework.gpu=0

To launch C2F-ARM+QTE:

python launch.py method=QTE rlbench.task=take_lid_off_saucepan rlbench.demo_path=/mnt/my/save/dir framework.gpu=0