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README.md

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+---
+license: bsd-3-clause
+tags:
+  - world-model
+  - dynamics-model
+  - robotics
+  - manipulation
+datasets:
+  - villekuosmanen/bin_pick_pack_coffee_capsules
+---
+
+# Visual World Model — bin-pick-pack (proprio + visual latents)
+
+MLP-based world model (`SystemDynamicsEnsemble`) trained on the bin-pick-pack-coffee-capsules manipulation dataset. Predicts next proprioceptive state (17D) and next visual latent (32D) given a history of states, actions, and visual latents.
+
+## Architecture
+
+- **Type**: MLP ensemble (2 heads)
+- **State dim**: 17 (7 joint positions + 10 EEF pose as xyz/rot6d/gripper)
+- **Action dim**: 17 (same decomposition)
+- **Visual dim**: 32 (LAM-encoded visual latents from front camera)
+- **History horizon**: 2
+- **Forecast horizon**: 1
+- **Checkpoint size**: 1.9 MB
+
+## Training
+
+- **Dataset**: [villekuosmanen/bin_pick_pack_coffee_capsules](https://huggingface.co/datasets/villekuosmanen/bin_pick_pack_coffee_capsules) — 47865 frames, 200 episodes
+- **Visual latents**: Precomputed from fine-tuned LAM encoder ([pravsels/lam-binpack-finetune](https://huggingface.co/pravsels/lam-binpack-finetune))
+- **Split**: 35387 train / 12078 val sequences (val_ratio=0.25, seed=0)
+- **Epochs**: 50
+- **Batch size**: 64
+- **Learning rate**: 3e-4 (cosine schedule, min_lr=3e-5)
+- **Final train loss**: 0.09442
+- **Final val loss**: 0.09916
+- **Visual loss**: 0.07207
+- **W&B**: [pravsels/binpack-world-model/runs/2pq0n2mx](https://wandb.ai/pravsels/binpack-world-model/runs/2pq0n2mx)
+
+## Files
+
+| File | Description |
+|------|-------------|
+| `best.pt` | Best checkpoint (epoch 50) |
+| `config.yaml` | Training configuration (Isambard) |
+
+## Checkpoint format
+
+```python
+checkpoint = torch.load("best.pt", map_location="cpu")
+# checkpoint["model_state_dict"] -> SystemDynamicsEnsemble.load_state_dict()
+# checkpoint["epoch"], checkpoint["train_loss"], checkpoint["val_loss"], etc.
+```
+
+## Integrity
+
+```
+sha256: 050dfaffa2c98ff112d6a0d2eba738328bac8b3934863bfecff59d62bd2d2410  best.pt
+```
+
+Verified by running `sha256sum` twice on the source file.
+
+## Usage
+
+```python
+from rsl_rl.offline.offline_world_model_trainer import build_system_dynamics_model
+import torch
+
+model = build_system_dynamics_model(
+    state_dim=17, action_dim=17, visual_dim=32,
+    ensemble_size=2, history_horizon=2, device="cpu",
+)
+ckpt = torch.load("best.pt", map_location="cpu")
+model.load_state_dict(ckpt["model_state_dict"])
+model.eval()
+
+# Single-step prediction
+# state_hist: (1, 2, 17), action_hist: (1, 2, 17), visual_hist: (1, 2, 32)
+state_pred, _, _, _, _, _, visual_pred = model(state_hist, action_hist, visual_hist)
+```