Safe Learning on Real Robots

A collection of algorithms and experiment tools for safe sim to real transfer and learning in robotics.

Features

• Three different CMDP solvers, CRPO, Saute-RL and primal-dual, compatible with (variants of) Brax's SAC, MBPO and PPO.
• Algorithm implementation is interchangeable between training in simulation to training on real robots via OnlineEpisodeOrchestrator. Check out rccar_experiments for a full example. Support for training online on any real robot supported by MuJoCo Playground, including Unitree Go1/2.
• Fast training. Full compatibility with MuJoCo Playground. Reimplementation of OpenAI's Safety Gym in MJX and safety tasks from Real-World RL suite.

Requirements

• Python == 3.11.6
• uv (recommended) or the built-in venv

Installation

Using pip

git clone https://github.com/yardenas/safe-learning
cd safe-learning
python3 -m venv venv
source venv/bin/activate
pip install -e .

Using uv

Install uv if it is not already available:

curl -LsSf https://astral.sh/uv/install.sh | sh

Create a project environment and install dependencies:

git clone https://github.com/yardenas/safe-learning
cd safe-learning
uv sync
uv run python --version  # sanity check, optional

Installing madrona_mjx (optional, required for Madrona backend)

Some benchmarks (e.g., the MJX-based pick-and-place tasks) require the custom madrona_mjx fork. Build and install it inside the UV environment you created above:

1. From the parent directory of safe-sim2real, clone the repository and check out the tested commit:

   git clone https://github.com/shacklettbp/madrona_mjx.git
   cd madrona_mjx
   git checkout c34f3cf6d95148dba50ffeb981aea033b8a4d225
   git submodule update --init --recursive

2. Configure and build (disable Vulkan if you do not have it available):

   mkdir -p build
   cd build
   cmake -DLOAD_VULKAN=OFF ..
   cmake --build . -j
   cd ..

3. While having your environment activated, install the Python bindings into your UV environment:

   uv pip install -e .

Refer to the upstream repository for platform-specific prerequisites (CUDA, Vulkan, compiler versions). Re-run uv pip install -e . whenever you rebuild the library.

Troubleshooting tips

• If you see CUDA OOMs immediately after the build, try export MADRONA_DISABLE_CUDA_HEAP_SIZE=1 before launching training.

• Populate the kernel caches to avoid recompilation on every run:

  export MADRONA_MWGPU_KERNEL_CACHE=/path/to/cache/mwgpu
  export MADRONA_BVH_KERNEL_CACHE=/path/to/cache/bvh

Usage

Our code uses Hydra to configure experiments. Each experiment is defined as a yaml file in ss2r/configs/experiments. For example, to train a Unitree Go1 policy with a constraint on joint limit:

python train_brax.py +experiment=go1_sim_to_real

Docs

• Policies (in onnx format) used for the Unitree Go1 experiments can be found in ss2r/docs/policies.
• In ss2r/docs/videos you can find videos of 5 trials for each policy, marked by its policy id.

Citation

If you find our repository useful in your work, please consider citing:

@inproceedings{
  as2025spidrsimpleapproachzeroshot,
  title={{SP}i{DR}: A Simple Approach for Zero-Shot Safety in Sim-to-Real Transfer},
  author={Yarden As and Chengrui Qu and Benjamin Unger and Dongho Kang and Max van der Hart and Laixi Shi and Stelian Coros and Adam Wierman and Andreas Krause},
  booktitle={International Conference on Neural Information Processing Systems},
  year={2025},
}