rewardlearning-robot

This codebase interfaces with a Franka Panda robot through polymetis and runs an implementation of DroQ from A Walk in the Park.

setup

# copy code
git clone git@github.com:dxyang/rewardlearning-robot.git
git submodule init
git submodule update

# create the conda python environment, install most dependencies
conda env create -f environment.yml
pip install --upgrade pip

# install pytorch
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113

# install jax
pip install --upgrade "jax[cuda]==0.4.2" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

# install walk_in_the_park
cd walk_in_the_park; pip install -e .

# install r3m
cd r3m; pip install -e .

code structure

cam - wrapper to interface with a realsense camera via pyrealsense2
reward_extraction - learned reward function model and training code
robot - gym like interface to setup and interact with a robot
viz - some code for visualizing SE3 poses and pointclouds
walk_in_the_park - git submodule. train_online.py is the main workhorse that we use. other parts of the codebase also pull from the replay buffer
r3m - git submodule. useful for extracting features from images.

useful example commands

# record demos
python -m robot.record --max_time_per_demo=15 --task test --show_viewer

# train agent
python -m walk_in_the_park.train_online