Webpage: https://serl-robot.github.io/
SERL provides a set of libraries, env wrappers, and examples to train RL policies for robotic manipulation tasks. The following sections describe how to use SERL. We will illustrate the usage with examples.
🎬: SERL video, additional video on sample efficient RL.
Table of Contents
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Setup Conda Environment: create an environment with
conda create -n serl python=3.10
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Install Jax as follows:
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For CPU (not recommended):
pip install --upgrade "jax[cpu]" -
For GPU: (change cuda12 to cuda11 if you are using older driver versions)
pip install --upgrade "jax[cuda12_pip]" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html -
For TPU
pip install --upgrade "jax[tpu]" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html -
See the Jax Github page for more details on installing Jax.
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Install the serl_launcher
cd serl_launcher pip install -e . pip install -r requirements.txt
SERL provides a set of common libraries for users to train RL policies for robotic manipulation tasks. The main structure of running the RL experiments involves having an actor node and a learner node, both of which interact with the robot gym environment. Both nodes run asynchronously, with data being sent from the actor to the learner node via the network using agentlace. The learner will periodically synchronize the policy with the actor. This design provides flexibility for parallel training and inference.
Table for code structure
| Code Directory | Description |
|---|---|
| serl_launcher | Main code for SERL |
| serl_launcher.agents | Agent Policies (e.g. DRQ, SAC, BC) |
| serl_launcher.wrappers | Gym env wrappers |
| serl_launcher.data | Replay buffer and data store |
| serl_launcher.vision | Vision related models and utils |
| franka_sim | Franka mujoco simulation gym environment |
| serl_robot_infra | Robot infra for running with real robots |
| serl_robot_infra.robot_servers | Flask server for sending commands to robot via ROS |
| serl_robot_infra.franka_env | Gym env for real franka robot |
We provide a simulated environment for trying out SERL with a franka robot.
Check out the Quick Start with SERL in Sim
- Training from state observation example
- Training from image observation example
- Training from image observation with 20 demo trajectories example
We provide a step-by-step guide to run RL policies with SERL on the real Franka robot.
Check out the Run with Franka Arm on Real Robot
We welcome contributions to this repository! Fork and submit a PR if you have any improvements to the codebase. Before submitting a PR, please run pre-commit run --all-files to ensure that the codebase is formatted correctly.
If you use this code for your research, please cite our paper:
@misc{luo2024serl,
title={SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning},
author={Jianlan Luo and Zheyuan Hu and Charles Xu and You Liang Tan and Jacob Berg and Archit Sharma and Stefan Schaal and Chelsea Finn and Abhishek Gupta and Sergey Levine},
year={2024},
eprint={2401.16013},
archivePrefix={arXiv},
primaryClass={cs.RO}
}