Contributors: Songlin Wei, Zhenhao Ni, Jie Liu, Zhenyu Zhao and more (to appear) ...

What is SIMPLE?

SIMPLE stands for SIMulation-based Policy Learning and Evaluation.

It is a simple simulation environment supports:

• multiple agents: (franka arm/aloha bimanual arms/dexmate wheeled robot and unitree g1 humanoid!)
• 1000+ Objaverse assets
• 50+ Habitat HSSD scenes
• 50+ humanoid wholebody loco-manipulation tasks

System Requirements

SIMPLE is built on top of IsaacSim 4.5 and MuJoCo 3.3.

Component	Minimum	Recommended
OS	Ubuntu 22.04	Ubuntu 22.04
CPU	Intel Core i7 / AMD Ryzen 7	Intel Core i9 / AMD Ryzen 9
RAM	32 GB	64 GB
GPU	NVIDIA RTX 2070 (8 GB VRAM)	NVIDIA RTX 3080 Ti / 4090 (16+ GB VRAM)
NVIDIA Driver	535.x	Latest
CUDA	12.x	12.x
Python	3.10	3.10
Storage	50 GB SSD	100+ GB NVMe SSD

An RTX-class NVIDIA GPU is required. GTX and older architectures are not supported.

Installation

Clone the project:

git clone git@github.com:physical-superintelligence-lab/SIMPLE.git

Change directory to the project root:

cd SIMPLE

Pull all submodules

git submodule update --init --recursive

We offer three options for setting up SIMPLE:

[Option 1] UV setup (Quickest)

Install uv if not already done

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

Install all dependencies at once

UV_HTTP_TIMEOUT=3000 GIT_LFS_SKIP_SMUDGE=1 uv sync --all-groups --index-strategy unsafe-best-match

Install CuRobo

bash scripts/install_curobo.sh

Activate the environment:

source .venv/bin/activate

Verify the installation by printing the version number

python -c "import simple; print(simple.__version__)"

[Optional] Build the docs.

make live

Open http://127.0.0.1:8000 in a browser to view the documentation.

The document are working in progress. Feel free to raise questions using github issue, we will try to complete the document construction as soon as possible.

[Option 2] Nix setup

We recommend using nix on fresh new linux host, otherwise, if you alread have install NVIDIA driver and CUDA, it will be faster to setup SIMPLE through uv.

Nix is a modern package manager and build system that focuses on reproducibility, isolation, and declarative system configuration.

Instead of installing software directly into your system (like apt or pip), Nix builds everything in isolated environments and stores them in the /nix/store, where each package version is uniquely identified by a hash.

Nix Setup (Recommend on fresh new hosts)

1. Install Nix first, for all interactive questions, enter y:

sh <(curl --proto '=https' --tlsv1.2 -L https://nixos.org/nix/install) --daemon

2. After Nix installation, open up a new shell to proceed.

If you encounter issues with nix command not found, try

export PATH=/nix/var/nix/profiles/default/bin:$PATH

3. Pull git modules recursively

git submodule update --init --recursive

Run the prerequisite check once on a new host:

./scripts/nix/prereq-check.sh

nix develop auto-bootstraps dependencies on first entry (or when uv.lock / pyproject.toml changes).

Start the dev shell:

nix --extra-experimental-features "nix-command flakes" develop

Or run a single command inside the dev shell:

env -u LD_LIBRARY_PATH nix --extra-experimental-features "nix-command flakes" develop -c <command>

Do not activate the virtual environment directly with source .venv/bin/activate or source .venv-nix/bin/activate. This repo expects the Nix shell and the Python environment to be used together. The virtual environment alone is not a supported runtime. If your IDE terminal auto-sources .venv-nix/bin/activate, disable that behavior for this workspace or deactivate before entering through nix develop.

Check if install successfully.

python -c "import simple; print(simple.__version__)"

You should see version number printed.

• If encouter installtion or running issues, please checkout Troubleshootings in the Docs

Download a task

export task=G1WholebodyXMovePickTeleop-v0
hf download USC-PSI-Lab/psi-data simple-eval/$task.zip --local-dir=./data/evals/ --repo-type=dataset
unzip data/evals/simple-eval/$task.zip -d data/evals/simple-eval

Running evaluation

CLI usage is supported, but it is not the preferred integration surface.

export task=G1WholebodyXMovePickTeleop-v0
export entry=eval_decoupled_wbc
export agent=gr00t_n16_decoupled_wbc
export dr=level-0

env -u LD_LIBRARY_PATH nix --extra-experimental-features 'nix-command flakes' develop -c \
  python -m simple.cli.$entry simple/$task $agent $dr \
  --data-format lerobot \
  --data-dir data/evals/simple-eval/$task/$dr \
  --host 127.0.0.1 \
  --port 21000 \
  --headless

Evalating on tasks collected using the SONIC stack

 TASK_NAME=G1WholebodyXMovePickTeleop-v0 uv run eval-decoupled-wbc simple/G1WholebodyXMovePickTeleop-v0 gr00t_n16_decoupled_wbc train --data-format lerobot --data-dir data/evals/simple-eval/G1WholebodyXMovePickTeleop-v0/level-0 --host 127.0.0.1 --port 21000 --headless

Nix Notes

The Nix runtime is documented in detail in docs/source/nix-runtime.md.

Short version:

• Mutually exclusive with Docker.
• Intended host baseline: Linux with NVIDIA drivers already installed, especially Ubuntu hosts.
• Run ./scripts/nix/prereq-check.sh first on a new host.
• Nix owns userspace; the host only owns the NVIDIA driver boundary.
• The shell fails early on runtime pollution from LD_LIBRARY_PATH, PYTHONPATH, PYTHONHOME, or LD_PRELOAD.
• The default Python environment is .venv-nix.
• Bootstrap entry points are ./scripts/nix/bootstrap-python.sh, ./scripts/nix/bootstrap-gpu.sh, and ./scripts/nix/bootstrap.sh.
• Prefer importing simple as a library from inside the dev shell; treat the CLI as a thin convenience layer.

Operational notes:

• Remove a root-owned .venv left by older Docker runs with sudo rm -rf .venv.
• Use SIMPLE_AUTO_BOOTSTRAP=0 to skip auto-setup, or SIMPLE_FORCE_BOOTSTRAP=1 to force re-bootstrap.
• If you need to run nix from inside the dev shell, prefer env -u LD_LIBRARY_PATH nix --extra-experimental-features "nix-command flakes" ....

[Option 3] Docker setup

We also support building and running SIMPLE in docker. Please refer to the documents for docker setup.

📊 Simulation Benchmarking Results

This is a preliminary benchmark with 6 tasks accompanying the Psi-0 project. Please also checkout Psi-0 for more details of intergrating Psi-0 with SIMPLE.

To rigorously evaluate the robustness and generalization of the learned policies, we design three evaluation levels with progressive out-of-distribution variations applied to the training environment:

The evaluation environments are provided in the huggingface repository USC-PSI-Lab/psi-data.

• Level 0 (Visual & Distractors): Randomizes table materials and the types/initial positions of distractor objects.
• Level 1 (Lighting): Includes Level 0 variations + extreme changes in lighting conditions.
• Level 2 (Spatial pose): Includes Level 1 variations + perturbations to the initial positions of the target objects.

Success rates are reported out of 10 evaluation trials per level (Level 0 | Level 1 | Level 2).

Baseline / Task	G1Wholebody XMove PickTeleop-v0	G1Wholebody BendPickMP-v0	G1Wholebody Handover Teleop-v0	G1Wholebody Locomotion PickBetweenTables Teleop-v0	G1Wholebody Tabletop GraspMP-v0	G1Wholebody XMove BendPick Teleop-v0
Psi0	10 | 10 | 6	10 | 10 | 10	7 | 7 | 10	7 | 5 | 6	10 | 10 | 8	10 | 9 | 9
GR00T N1.6	10 | 10 | 7	7 | 7 | 6	1 | 3 | 3	0 | 0 | 0	9 | 9 | 7	4 | 4 | 1
OpenPi π0.5	7 | 5 | 1	10 | 10 | 8	5 | 4 | 5	3 | 3 | 3	10 | 10 | 8	0 | 0 | 0
InternVLA-M1	0 | 0 | 0	5 | 5 | 0	0 | 0 | 0	0 | 0 | 0	0 | 0 | 0	3 | 5 | 7
H-RDT	0 | 0 | 2	0 | 0 | 1	0 | 1 | 0	0 | 0 | 0	0 | 0 | 0	0 | 0 | 0
DreamZero	- | - | -	- | - | -	- | - | -	- | - | -	9 | 10 | 10	- | - | -
EgoVLA	0 | 1 | 2	7 | 5 | 8	0 | 4 | 3	0 | 0 | 0	10 | 10 | 7	3 | 5 | 4
Diff. Policy	3 | 3 | 2	10 | 8 | 6	3 | 2 | 4	4 | 0 | 0	8 | 9 | 8	0 | 0 | 0
ACT	10 | 10 | 5	10 | 9 | 9	7 | 7 | 10	5 | 5 | 5	10 | 10 | 8	9 | 10 | 10

Citation

@misc{wei2026psi0,
  title={$\Psi_0$: An Open Foundation Model Towards Universal Humanoid Loco-Manipulation}, 
  author={Songlin Wei and Hongyi Jing and Boqian Li and Zhenyu Zhao and Jiageng Mao and Zhenhao Ni and Sicheng He and Jie Liu and Xiawei Liu and Kaidi Kang and Sheng Zang and Weiduo Yuan and Marco Pavone and Di Huang and Yue Wang},
  year={2026},
  eprint={2603.12263},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  url={https://arxiv.org/abs/2603.12263}, 
}

License

This project is licensed under the MIT.

See the LICENSE file for details.