Document low-latency SONIC release

README.md

@@ -144,6 +144,8 @@ All checkpoints (ONNX format) are available directly in this repository. Inferen
 | Policy encoder | `model_encoder.onnx` | Encodes motion reference into latent |
 | Policy decoder | `model_decoder.onnx` | Decodes latent into joint actions |
 | Kinematic planner | `planner_sonic.onnx` | Real-time locomotion style planner |
+| Low-latency policy | `low_latency/model_encoder.onnx`, `low_latency/model_decoder.onnx`, `low_latency/observation_config.yaml` | Reduced-lookahead G1 controller variant |
+| Low-latency PyTorch checkpoint | `low_latency/last.pt` | Training checkpoint and config for the low-latency variant |
 
 **Quick download** (requires `pip install huggingface_hub`):
 
@@ -157,10 +159,48 @@ Or use the download script from the GitHub repo:
 ```bash
 python download_from_hf.py             # policy + planner (default)
 python download_from_hf.py --no-planner # policy only
+python download_from_hf.py --low-latency # low-latency policy + planner
 ```
 
 See the [Download Models guide](https://nvlabs.github.io/GR00T-WholeBodyControl/getting_started/download_models.html) for full instructions.
 
+## Low-Latency Inference
+
+The low-latency SONIC variant is stored under `low_latency/` and does not
+replace the default top-level policy. To run it with the C++ deployment stack:
+
+```bash
+git clone https://github.com/NVlabs/GR00T-WholeBodyControl.git
+cd GR00T-WholeBodyControl
+pip install huggingface_hub
+python download_from_hf.py --low-latency
+```
+
+Simulation:
+
+```bash
+cd gear_sonic_deploy
+./deploy.sh \
+    --cp policy/low_latency/model \
+    --obs-config policy/low_latency/observation_config.yaml \
+    sim
+```
+
+Real robot VLA or teleoperation:
+
+```bash
+cd gear_sonic_deploy
+./deploy.sh \
+    --cp policy/low_latency/model \
+    --obs-config policy/low_latency/observation_config.yaml \
+    --input-type zmq_manager \
+    real
+```
+
+`deploy.sh` expects `--cp` to be the shared model prefix and appends
+`_encoder.onnx` and `_decoder.onnx` internally. For the full VLA deployment
+flow, see the [VLA Inference guide](https://nvlabs.github.io/GR00T-WholeBodyControl/tutorials/vla_inference.html).
+
 ## Documentation
 
 📚 **[Full Documentation](https://nvlabs.github.io/GR00T-WholeBodyControl/)**
@@ -239,4 +279,4 @@ This work builds upon and acknowledges:
 
 ## Model Card Contact
 
-For questions about this model card or responsible AI considerations, contact: [gear-wbc@nvidia.com](mailto:gear-wbc@nvidia.com)
+For questions about this model card or responsible AI considerations, contact: [gear-wbc@nvidia.com](mailto:gear-wbc@nvidia.com)