Model Card

Model Card#

SONIC provides two released whole-body controller checkpoints for the Unitree G1. Choose the model based on whether you want the original general-purpose controller or reduced reference lookahead for teleoperation.

Available Models#

Model

Hugging Face location

SMPL reference input

Intended use and comments

Default SONIC (original release)

Top-level model_encoder.onnx, model_decoder.onnx, and observation_config.yaml; training checkpoint at sonic_release/last.pt

10 future frames at 20 ms spacing, approximately 200 ms of reference lookahead

Default general-purpose SONIC controller for motion tracking, planning, teleoperation, and compatibility with existing deployments. G1 and teleoperation future-reference observations use step5.

Low-latency teleoperation

low_latency/

4 future frames at 20 ms spacing, approximately 80 ms of reference lookahead

Intended for more responsive whole-body teleoperation and VLA execution. G1 and teleoperation future-reference observations use step1. Use its encoder, decoder, and observation config together.

Both models use the SONIC universal-token controller, produce 64-dimensional latent motion tokens, run the controller at 50 Hz, and support SMPL pose, G1 motion reference, and VR 3-point inputs. Deployment uses C++ and TensorRT; the PyTorch checkpoints support Isaac Lab evaluation and continued training.

Note

The lookahead values describe the reference horizon presented to the controller. They are not measurements of total end-to-end teleoperation latency, which also includes sensing, networking, preprocessing, and inference.

Released Files#

Model

Deployment files

PyTorch and configuration files

Default SONIC

model_encoder.onnx, model_decoder.onnx, observation_config.yaml

sonic_release/last.pt, sonic_release/config.yaml

Low-latency teleoperation

low_latency/model_encoder.onnx, low_latency/model_decoder.onnx, low_latency/observation_config.yaml

low_latency/last.pt, low_latency/config.yaml, low_latency/model_config.yaml

All files are hosted in nvidia/GEAR-SONIC. Model weights are covered by the NVIDIA Open Model License.

Choosing a Model#

Use Default SONIC when you want the original release, the broadest compatibility with existing deployment setups, or the standard motion-tracking and planning controller.

Use Low-latency teleoperation when responsiveness to streamed SMPL, VR, or VLA commands is the priority. Its shorter reference horizon reduces commanded motion lookahead, but it does not remove latency elsewhere in the system.

Usage#

Install the Hugging Face dependency from the repository root:

pip install huggingface_hub

Default SONIC#

python download_from_hf.py

cd gear_sonic_deploy
./deploy.sh --input-type zmq_manager real

Low-Latency Teleoperation#

python download_from_hf.py --low-latency

cd gear_sonic_deploy
./deploy.sh \
    --cp policy/low_latency/model \
    --obs-config policy/low_latency/observation_config.yaml \
    --input-type zmq_manager \
    real

Python VLA Launcher#

For the default model:

python gear_sonic/scripts/launch_inference.py \
    --camera-host 192.168.123.164 \
    --prompt "pick up the cup"

For the low-latency model:

python gear_sonic/scripts/launch_inference.py \
    --deploy-checkpoint policy/low_latency/model \
    --deploy-obs-config policy/low_latency/observation_config.yaml \
    --camera-host 192.168.123.164 \
    --prompt "pick up the cup"

See Downloading Model Checkpoints for PyTorch checkpoint evaluation and additional download options.

Limitations and Safety#

  • The low-latency name refers to reduced controller reference lookahead, not a benchmark of total system latency.

  • Each ONNX encoder and decoder must be used with its matching observation configuration.

  • These checkpoints target the Unitree G1 embodiment.

  • Test in simulation before deployment and keep a safety operator ready to stop a physical robot.

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