Install Teleopit

Install only the parts you need. All commands below run from the repository root and require Python 3.10 or newer.

1. Get the Code

git clone https://github.com/BotRunner64/Teleopit.git
cd Teleopit

You only need Git submodules for a physical G1 or optional LinkerHand control; those steps appear later on this page.

2. Create a Python Environment

Choose one environment tool. Do not run all three sections.

uv

uv venv --python 3.10
source .venv/bin/activate

When this page shows pip install, you may use uv pip install instead.

pip and venv

python3.10 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip

Conda

conda create -n teleopit python=3.10
conda activate teleopit

Conda creates the environment; use pip install inside that environment to install Teleopit.

3. Install the Profile You Need

Each extra includes the base Teleopit package. Start with the row matching your goal; you can install another extra later in the same environment.

Goal	Install command	What it adds
Run a motion controller in MuJoCo	pip install -e .	Core inference, GMR, MuJoCo and ONNX Runtime
Use Pico in simulation or on G1	pip install -e '.[pico4]'	Pico receiver plus the sim2real runtime
Replay BVH on a physical G1 without Pico	pip install -e '.[sim2real]'	G1 runtime and OpenCV
Train a controller	pip install -e '.[train]'	mjlab, RSL-RL and experiment loggers
Record Pico sim2real episodes	pip install -e '.[recording]'	Pico runtime and MP4 writing
Review saved recordings	pip install -e '.[review]'	OpenCV and the MuJoCo/Viser reviewer
Use OpenNeck with Pico	pip install -e '.[openneck]'	Pico runtime and the OpenNeck driver
Run the test suite	pip install -e '.[dev]'	pytest and coverage tools

4. Download the Matching Assets

The Python package does not contain robot meshes, policies or motion datasets. Install the default ModelScope downloader once:

pip install modelscope

Then download the bundle for your goal:

Goal	Command
Simulation, Pico VR or G1 inference	python scripts/setup/download_assets.py --only robots gmr ckpt bvh
Training from the distributed datasets	python scripts/setup/download_assets.py --only robots data
Everything	python scripts/setup/download_assets.py

Use HuggingFace instead of ModelScope when needed:

python scripts/setup/download_assets.py \
    --source huggingface \
    --only robots gmr ckpt bvh

The inference bundle creates the track_g1 and track_g1_neck_o6 ONNX/checkpoint pairs under ckpt/, plus the G1 model files, GMR files and a sample BVH under their expected project paths. See Assets for the complete inventory and asset group mapping.

5. Additional Setup for a Physical G1

Build the C++ DDS bridge on the computer that will run Teleopit:

git submodule update --init --recursive
bash scripts/setup/setup_g1_bridge.sh

The bridge is required for both Pico and BVH control on a real G1. See Companion Projects if the build or robot connection fails.

6. Optional Hardware

LinkerHand L6 or O6

Only install these local packages when hands.enabled=true:

git submodule update --init --recursive
pip install -e third_party/linkerhand-python-sdk
pip install -e third_party/somehand
bash scripts/setup/download_somehand_assets.sh

OpenNeck

The openneck extra already includes the Pico profile. Calibrate the device before enabling it:

pip install -e '.[openneck]'
openneck calibrate

Teleopit uses the OpenNeck angle API. Old normalized calibration fields are not supported.

RealSense Recording or Preview

Install pyrealsense2 separately when a RealSense camera is enabled. On Arm machines, use conda-forge:

conda install -c conda-forge pyrealsense2

Pico body tracking itself does not require RealSense.

7. Verify the Environment

Run the core import check:

python -c "import teleopit; print('teleopit OK')"

If you installed Pico or training dependencies, run the matching check:

python -c "from pico_bridge import PicoBridge; print('Pico OK')"
python -c "import train_mimic.tasks; print('training OK')"

For an inference profile with the robots gmr ckpt bvh assets, finish with one sample simulation:

python scripts/run/run_sim.py \
    controller.policy_path=ckpt/track_g1.onnx \
    input.bvh_file=data/sample_bvh/aiming1_subject1.bvh

The installation is ready when a MuJoCo window opens and the simulated G1 follows the sample motion. Close the window to stop, then continue with one of the four task-based tutorials.