Remove robotic_grounding and dependent task_library_loader for release

.github/workflows/docs.yml

@@ -56,7 +56,7 @@ jobs:
     timeout-minutes: 20
     # sphinx-multiversion runs `git worktree add` for each version, which
     # materializes the full repo at that SHA and triggers Git LFS smudging
-    # on sibling-package assets (parquet / pkl / npz under robotic_grounding/).
+    # on large sibling-package LFS assets (parquet / pkl / npz).
     # We never need those file contents to build docs -- skipping smudging
     # keeps them as LFS pointer files in the worktree.
     env:

README.md

@@ -8,31 +8,30 @@ Monorepo for **Video to Data (V2D)** — an end-to-end pipeline that converts hu
  ┌───────────────┐   ┌──────────────────────┐   ┌──────────────────────┐   ┌────────────────────────────┐
  │ Human demo    │ → │ 1. Video Ingestion   │ → │ 2. Reconstruction    │ → │ 3. Robotic Grounding       │
  │ video / rosbag│   │    Agent             │   │ depth · masks ·      │   │ retargeting → Isaac Lab    │
- │               │   │ action segments ·    │   │ meshes · 6D pose ·   │   │ RL training (RSL-RL PPO)   │
- │               │   │ entity graph ·       │   │ SMPL body            │   │                            │
+ │               │   │ action segments ·    │   │ meshes · 6D pose ·   │   │ RL training                │
+ │               │   │ entity graph ·       │   │ body pose            │   │ Coming soon                │
  │               │   │ visual embeddings    │   │                      │   │                            │
  └───────────────┘   └──────────────────────┘   └──────────────────────┘   └────────────────────────────┘
                        video_ingestion_agent/      reconstruction/             robotic_grounding/
 ```
 
 1. **Video Ingestion Agent** — a LangGraph-driven agentic workflow that segments demonstration videos into temporally-bounded action clips, extracts an entity-relation scene graph, and stores per-frame SigLIP-2 embeddings. The result is a queryable action database (`graph.db` + `vector.db`) that lets downstream stages select which clips to process via natural-language retrieval, instead of brute-forcing the full video.
 2. **Reconstruction** — containerized vision modules turn the selected RGB (or stereo) clips into per-frame depth, object masks, textured meshes, 6-DoF object poses, and SMPL human body parameters. Multi-view pipelines (`run_mv_hoi_reconstruction`, `run_mv_calibration`) orchestrate the full reconstruction from a rosbag.
-3. **Robotic Grounding** — human motion (e.g. Arctic) is retargeted onto the target robot embodiment (Sharpa), then the reconstructed scene and retargeted motion drive Isaac Lab environments trained with RSL-RL PPO to produce deployable policies.
+3. **Robotic Grounding** — human motion is retargeted onto the target robot embodiment (Sharpa or G1), then the reconstructed scene and retargeted motion drive Isaac Lab environments trained with RL to produce deployable policies.
 
 ## Packages
 
 | Package | Role | Runtime |
 |---|---|---|
 | [`video_ingestion_agent/`](video_ingestion_agent/) | Video → action segments + entity scene graph + frame embeddings. LangGraph pipeline (segment → verify/refine → entity graph → embeddings) plus an EGAgent-style natural-language retrieval agent and an optional Gradio UI. | Python venv + vLLM server |
 | [`reconstruction/`](reconstruction/) | Video → depth, masks, meshes, 6D poses, human body. 18 containerized modules + multi-view pipelines. | Docker (per-module images) |
-| [`robotic_grounding/`](robotic_grounding/) | RL training on NVIDIA Isaac Lab 2.3.1 with RSL-RL (PPO); motion retargeting utilities. | Docker (`nvcr.io/nvstaging/isaac-amr`) |
+| [`robotic_grounding/`](robotic_grounding/) | RL training on NVIDIA Isaac Lab with PPO; motion retargeting utilities. **Code will be published in a later release.** | Coming soon |
 
 ## Prerequisites
 
 - Docker with GPU support ([install](https://docs.docker.com/engine/install/ubuntu/))
 - [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html)
 - Python 3.10+
-- NVIDIA driver 580.126.09 / CUDA 13.0 recommended (for `robotic_grounding`)
 
 ## Quickstart
 
@@ -95,27 +94,9 @@ See [reconstruction/README.md](reconstruction/README.md) for the complete module
 
 ### Robotic Grounding (data → RL policy)
 
-```bash
-cd robotic_grounding
-
-# One-time host setup (git-lfs, pre-commit)
-bash workflow/setup_deps.sh
-
-# Build + enter the Isaac Lab container
-./workflow/run.sh build  [version]
-./workflow/run.sh start  [version] [gpu_id]
-
-# Inside the container — train a policy
-python scripts/rsl_rl/train.py --task Sharpa-V2P-v0
-```
-
-See [robotic_grounding/README.md](robotic_grounding/README.md) for retargeting, debug environments, and task definitions.
-
-### Visualizer (retargeting gallery)
-
-Browse retargeted sequences as 3D animations at **http://10.111.83.14:8080/**
+The Robotic Grounding stage (motion retargeting + Isaac Lab RL training) will be publically available in a later release. See [robotic_grounding/README.md](robotic_grounding/README.md) for an overview.
 
-See [robotic_grounding/README.md#visualizer](robotic_grounding/README.md#visualizer) for setup instructions.
+The technical report and project website are available on the project page: [Webpage](https://nvidia-isaac.github.io/video_to_data/chord/). 
 
 ## Design Philosophy