Rename checkpoint release to sonic_v1_1

README.md

@@ -145,7 +145,7 @@ variant.
 |---|---|---|---|
 | **Default SONIC (original release)** | Top-level deployment files; PyTorch checkpoint under `sonic_release/` | 10 frames at 20 ms spacing, approximately 200 ms | General-purpose motion tracking, planning, and teleoperation. G1 and teleoperation references use `step5`. |
 | **Low-latency teleoperation** | `low_latency/` | 4 frames at 20 ms spacing, approximately 80 ms | Reduced-reference-lookahead controller for responsive teleoperation and VLA execution. G1 and teleoperation references use `step1`. |
-| **Heading/wrist teleoperation** | `heading_wrist/` | 10 frames at 20 ms spacing, approximately 200 ms | Robot-heading-normalized controller trained with wrist-pose augmentation for heading-stable 3-point teleoperation and SONIC-backed VLA policies. G1 and teleoperation references use `step5`; this is not the low-latency model. |
+| **SONIC v1.1** | `sonic_v1_1/` | 10 frames at 20 ms spacing, approximately 200 ms | Robot-heading-normalized controller trained with wrist-pose augmentation for heading-stable 3-point teleoperation and SONIC-backed VLA policies. G1 and teleoperation references use `step5`; this is not the low-latency model. |
 
 All variants use 64-dimensional universal motion tokens and a 29-DoF action
 decoder. Inference is powered by TensorRT and runs on both desktop and Jetson
@@ -158,8 +158,8 @@ hardware.
 | 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 |
-| Heading/wrist policy | `heading_wrist/model_encoder.onnx`, `heading_wrist/model_decoder.onnx`, `heading_wrist/observation_config.yaml` | Heading-normalized G1 controller variant |
-| Heading/wrist PyTorch checkpoint | `heading_wrist/last.pt` | Training checkpoint and configs for the heading/wrist variant |
+| SONIC v1.1 policy | `sonic_v1_1/model_encoder.onnx`, `sonic_v1_1/model_decoder.onnx`, `sonic_v1_1/observation_config.yaml` | Robot-heading-normalized G1 controller variant |
+| SONIC v1.1 PyTorch checkpoint | `sonic_v1_1/last.pt` | Training checkpoint and configs for SONIC v1.1 |
 
 **Quick download** (requires `pip install huggingface_hub`):
 
@@ -174,14 +174,14 @@ Or use the download script from the GitHub repo:
 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
-python download_from_hf.py --heading-wrist # heading/wrist policy + planner
+python download_from_hf.py --sonic-v1-1 # SONIC v1.1 policy + planner
 ```
 
 See the [Download Models guide](https://nvlabs.github.io/GR00T-WholeBodyControl/getting_started/download_models.html) for full instructions.
 
-## Heading/Wrist Inference
+## SONIC v1.1 Inference
 
-The `heading_wrist/` variant uses robot-heading-normalized target orientation
+The `sonic_v1_1/` variant uses robot-heading-normalized target orientation
 and a 10-frame SMPL/wrist reference horizon. It was trained with wrist-pose
 augmentation and is intended for 3-point teleoperation and SONIC-backed VLA
 policies trained against this controller.
@@ -190,12 +190,12 @@ policies trained against this controller.
 git clone https://github.com/NVlabs/GR00T-WholeBodyControl.git
 cd GR00T-WholeBodyControl
 pip install huggingface_hub
-python download_from_hf.py --heading-wrist
+python download_from_hf.py --sonic-v1-1
 
 cd gear_sonic_deploy
 ./deploy.sh \
-    --cp policy/heading_wrist/model \
-    --obs-config policy/heading_wrist/observation_config.yaml \
+    --cp policy/sonic_v1_1/model \
+    --obs-config policy/sonic_v1_1/observation_config.yaml \
     sim
 ```