Update README: 17 clean episodes, 2937 timesteps

README.md

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+# Rock Climb — Grasp-Taxonomy-Aware 3D Diffusion Policy
+
+Trains a DP3-style point cloud diffusion policy conditioned on grasp type (crimp/sloper/pinch/jug)
+to autonomously grasp climbing holds with a Franka arm + LEAP Hand.
+
+---
+
+## Quick Start (Training Machine)
+
+### 1. Clone the repo
+
+```bash
+git clone https://github.com/rumilog/rock-climb.git tele
+cd tele
+```
+
+### 2. Create a Python environment
+
+```bash
+python3 -m venv venv
+source venv/bin/activate
+pip install --upgrade pip
+```
+
+Install PyTorch with CUDA (adjust to match your GPU driver):
+
+```bash
+# For CUDA 11.8:
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu118
+
+# For CUDA 12.1:
+pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
+```
+
+Install remaining dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+### 3. Download the dataset from Hugging Face
+
+```bash
+mkdir -p datasets
+huggingface-cli download rlogh/climbing-holds-pointcloud --repo-type dataset --local-dir ./datasets/climbing_holds.zarr
+```
+
+Verify the download:
+
+```bash
+python3 -c "
+import zarr
+z = zarr.open('datasets/climbing_holds.zarr', 'r')
+print('Episodes:', z['meta/episode_ends'].shape[0])
+print('Timesteps:', z['data/state'].shape[0])
+print('Point cloud shape:', z['data/point_cloud'].shape)
+print('Grasp type IDs:', z['meta/grasp_type_id'][:5], '...')
+"
+```
+
+Expected output:
+```
+Episodes: 17
+Timesteps: 2937
+Point cloud shape: (2937, 1024, 3)
+Grasp type IDs: [3 3 3 3 3] ...
+```
+
+(grasp_type_id=3 is jug — this is a jug-only pilot dataset on hold 0, clean PC with z_min=0.006)
+
+### 4. Run training
+
+```bash
+cd data_collection
+
+python3 train.py \
+    --point-cloud \
+    --zarr ../datasets/climbing_holds.zarr \
+    --ckpt-dir ../checkpoints/pc_pilot \
+    --epochs 3000 \
+    --batch 128 \
+    --augment \
+    --good-only \
+    --save-every 100
+```
+
+Training writes to `../checkpoints/pc_pilot/`:
+- `best.pt` — EMA weights with lowest validation loss (use this for evaluation)
+- `epoch_XXXX.pt` — periodic snapshots
+- `norm_stats.json` — min-max normalization stats (required by evaluate.py)
+- `training_status.md` — live progress updated every 10 epochs
+
+### 5. Monitor training
+
+```bash
+cat ../checkpoints/pc_pilot/training_status.md
+```
+
+---
+
+## Training Details
+
+| Setting | Value |
+|---------|-------|
+| Architecture | PointNet encoder + 1D temporal U-Net (DP3-style) |
+| PointNet output | 256-d |
+| Grasp type conditioning | one-hot(4) → MLP → 64-d, fused with observation |
+| Conditioning vector | 512-d (PointNet 256 + State 128 + GraspType 64 + MLP) |
+| U-Net dims | (512, 1024, 2048) |
+| Optimizer | AdamW, lr=1e-4 |
+| LR schedule | 500-step cosine warmup |
+| EMA | Power-law warmup (power=0.75) |
+| Normalization | Min-max to [-1, 1] (DP3 convention) |
+| Diffusion | 100-step cosine DDPM (train), 10-step DDIM (inference) |
+| Obs horizon | 2 timesteps |
+| Pred horizon | 16 timesteps |
+| Action horizon | 8 timesteps |
+| Action dim | 23 (7 arm joints + 16 hand joints) |
+| Point cloud | 1024 pts, XYZ only, world frame, FPS downsampled |
+| Dataset | 17 episodes, 2937 timesteps, hold 0 (jug) — clean pilot (z_min=0.006) |
+
+---
+
+## Architecture
+
+```
+Point Cloud (1024×3) → PointNet → 256-d
+Robot State (2×23)   → MLP     → 128-d
+Grasp Type (one-hot) → MLP     → 64-d
+                       Concat → MLP → 512-d conditioning vector
+                                          ↓
+                              DDPM 1D Temporal U-Net
+                                          ↓
+                           Action chunk (16 × 23-dim)
+```
+
+Grasp type IDs: `0=crimp, 1=sloper, 2=pinch, 3=jug`
+
+---
+
+## Copying Checkpoints Back
+
+After training, copy the checkpoint back to the robot machine for evaluation:
+
+```bash
+scp -r checkpoints/pc_pilot/ user@robot-machine:/path/to/tele/checkpoints/pc_pilot/
+```
+
+Then on the robot machine:
+
+```bash
+source ~/franka/bin/activate
+source ~/frankapy/catkin_ws/devel/setup.bash
+cd ~/Desktop/tele/data_collection
+python3 evaluate.py --checkpoint ../checkpoints/pc_pilot/best.pt --hold 0 --grasp-type jug
+```
+
+---
+
+## Dataset Structure (zarr)
+
+```
+climbing_holds.zarr/
+  data/
+    state        (N, 23)       float32  — arm(7) + hand(16) joint positions
+    action       (N, 23)       float32  — same layout, shifted +1 timestep
+    point_cloud  (N, 1024, 3)  float32  — clean scene scan per episode, repeated per timestep
+    timestamps   (N,)          float64
+  meta/
+    episode_ends  (E,)  int64
+    hold_id       (E,)  int64   — 0=edge_A, 1=edge_B, 2=sloper, 3=pinch, 4=test_edge
+    quality       (E,)  int64   — 1=good, 0=bad
+    grasp_type    (E,)  str     — "crimp" | "sloper" | "pinch" | "jug"
+    grasp_type_id (E,)  int64   — 0=crimp, 1=sloper, 2=pinch, 3=jug
+```
+
+Note: images are NOT included in this dataset — the policy uses point clouds only.