[UPDATE]: add Train-Deploy Alignment module to README

Mark Train-Deploy Alignment as released, add update log entry,
check off to-do item, and replace Coming Soon placeholder with
full content (data augmentation, DAgger, inference quick start).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: ChonghaoSima

README.md

@@ -21,7 +21,7 @@
 
 - **[Model Arithmetic](#model-arithmetic)**: A weight-space merging strategy that combines models trained on different data subsets, efficiently capturing diverse knowledge without architectural complexity. **[Released]**
 - **[Stage Advantage](#stage-advantage)**: A stage-aware advantage estimator that provides stable, dense progress signals for policy training. **[Released]**
-- **[Train-Deploy Alignment](#train-deploy-alignment-coming-soon)**: Bridges the distribution gap via spatio-temporal augmentation, heuristic DAgger corrections, and temporal chunk-wise smoothing. **[Coming Soon]**
+- **[Train-Deploy Alignment](#train-deploy-alignment)**: Bridges the distribution gap via spatio-temporal augmentation, heuristic DAgger corrections, and temporal chunk-wise smoothing. **[Released]**
 
 χ₀ enables two sets of dual-arm robots to collaboratively orchestrate long-horizon garment manipulation — flattening, folding, and hanging — surpassing the state-of-the-art $\pi_{0.5}$ baseline by approximately 250% in success rate, with `only 20 hours of data and 8 A100 GPUs`.
 
@@ -47,13 +47,14 @@ https://github.com/user-attachments/assets/3f5f0c48-ff3f-4b9b-985b-59ad0b2ea97c
   - [Workflow](#workflow)
   - [Quick Start](#quick-start)
 - [Stage Advantage](#stage-advantage)
-- [Train-Deploy Alignment (Coming Soon)](#train-deploy-alignment-coming-soon)
+- [Train-Deploy Alignment](#train-deploy-alignment)
 - [Citation](#licenseandcitation)
 - [Troubleshooting](#troubleshooting)
 - [Links and Community](#links-and-community)
 
 ## Update
 
+- [Feb 15 2026] Release of the **Train-Deploy Alignment** module: data augmentation (time scaling, space mirroring), DAgger data collection, inference with temporal smoothing/ensembling and RTC, and HDF5-to-LeRobot conversion.
 - [Feb 14 2026] Release of the **Stage Advantage** module: advantage estimator training, evaluation, GT labeling, and AWBC training pipeline.
 - [Feb 10 2026] Initial release of the **Model Arithmetic** module with support for both JAX and PyTorch checkpoints (not tested thoroughly).
 - [Feb 10 2026] χ₀ paper released.
@@ -210,8 +211,8 @@ Checkpoints are written to the config’s checkpoint directory. You can then use
 - [x] kai0 oracle: training and inference code with non-advantage data of three tasks
 - [x] Model Arithmetic: code of different baselines for weight-space interpolation
 - [x] Stage Advantage: code, data (advantage labels), and checkpoints
-- [ ] HuggingFace & ModelScope: upload Stage Advantage data and checkpoints — **Feb 14**
-- [ ] Train-Deploy Alignment — **Feb 14**
+- [x] Train-Deploy Alignment: data augmentation, DAgger, inference (temporal smoothing, ensembling, RTC)
+- [ ] HuggingFace & ModelScope: upload Stage Advantage data and checkpoints
 
 ## Model Arithmetic
 
@@ -315,14 +316,56 @@ For a ready-to-use script with environment setup and automatic log management, s
 
 For the full pipeline details, configuration instructions, and all parameters, see [`stage_advantage/README.md`](stage_advantage/README.md).
 
-## Train-Deploy Alignment (Coming Soon)
+## Train-Deploy Alignment
 
-Train-Deploy Alignment bridges the distribution gap between training and real-world deployment through:
-- **Spatio-temporal augmentation**: Data augmentation including space mirroring and time scaling for dual-arm setups.
-- **Heuristic DAgger corrections**: Interactive on-robot data collection for iterative policy improvement.
-- **Temporal chunk-wise smoothing**: Smoothed action execution to reduce jitter during deployment.
+Train-Deploy Alignment bridges the distribution gap between training and real-world deployment through three sub-modules:
 
-**This module is currently under refinement and will be released soon.**
+- **Data Augmentation** (`train_deploy_alignment/data_augment/`): Time scaling (frame extraction at configurable rates), space mirroring (left/right arm swap + video flip), dataset merging, and HDF5-to-LeRobot format conversion.
+- **DAgger** (`train_deploy_alignment/dagger/`): Policy-in-the-loop data collection for both Agilex Piper and ARX X5 platforms. Operators run inference, switch to DAgger mode for human corrections, and save episodes (HDF5 + optional videos + intervention labels).
+- **Inference** (`train_deploy_alignment/inference/`): Deployment code for Agilex and ARX robots with multiple execution modes — synchronous, temporal smoothing, temporal ensembling, and **RTC (real-time chunking)**. Uses a two-machine setup (GPU policy server + robot IPC client).
+
+### Quick Start
+
+**Data Augmentation — Time scaling:**
+
+```bash
+python train_deploy_alignment/data_augment/time_scaling.py \
+  --src_path /path/to/source --tgt_path /path/to/extracted --repo_id extracted_dataset \
+  --extraction_factor 2
+```
+
+**Data Augmentation — Space mirroring (mirror + merge):**
+
+```bash
+python train_deploy_alignment/data_augment/space_mirroring.py full \
+  --src-path /path/to/original --mirror-path /path/to/mirrored --merge-path /path/to/merged \
+  --repo-id my_dataset
+```
+
+**DAgger — Agilex:** Start the policy server on the GPU host, then on the IPC:
+
+```bash
+conda activate kai0_inference
+python train_deploy_alignment/dagger/agilex/agilex_openpi_dagger_collect.py \
+  --host <gpu_host_ip> --port 8000 --ctrl_type joint --use_temporal_smoothing --chunk_size 50 \
+  --dataset_name <your_dataset_name>
+```
+
+**Inference — Agilex (temporal smoothing):** Start the policy server on the GPU host, then on the IPC:
+
+```bash
+conda activate kai0_inference
+python inference/agilex_inference_openpi_temporal_smoothing.py \
+  --host <gpu_host_ip> --port 8000 --ctrl_type joint --use_temporal_smoothing --chunk_size 50
+```
+
+**Inference — ARX (RTC mode):** Start the policy server with an RTC config, then on the IPC:
+
+```bash
+python inference/arx_openpi_inference_rtc.py --host <gpu_host_ip> --port 8000 --rtc_mode --chunk_size 50
+```
+
+For full setup instructions (IPC environment, CAN, ROS/ROS2, platform-specific details), see [`train_deploy_alignment/README.md`](train_deploy_alignment/README.md).
 
 ## License and Citation