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README.md

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+---
+license: mit
+tags:
+  - robotics
+  - vjepa2
+  - dm_control
+  - world-model
+  - teach-by-showing
+---
+
+# V-JEPA 2 Robot Multi-Task Dataset & Models
+
+Vision-based robot control data using **V-JEPA 2** (ViT-L) latent representations
+from DeepMind Control Suite environments.
+
+## 📊 Dataset
+
+| Task | Episodes | Transitions | Latent Dim | Action Dim | Success Rate |
+|------|----------|-------------|------------|------------|-------------|
+| reacher_easy | 1,000 | 200,000 | 1024 | 2 | 28.9% |
+| point_mass_easy | 1,000 | 200,000 | 1024 | 2 | 0.6% |
+| cartpole_swingup | 1,000 | 200,000 | 1024 | 1 | 0.0% |
+
+Each `.npz` file contains:
+- `z_t` — V-JEPA 2 latent state embeddings (N × 1024)
+- `a_t` — actions taken (N × action_dim)
+- `z_next` — next-state latent embeddings (N × 1024)
+- `rewards` — per-step rewards (N,)
+
+## 🤖 Models
+
+For each task, we provide:
+- **5× Dynamics Ensemble** — `dyn_0.pt` to `dyn_4.pt` (MLP: z + a → z_next, ~1.58M params each)
+- **1× Reward Model** — `reward.pt` (MLP: z + a → reward, ~329K params)
+
+### Architecture
+- Dynamics: `Linear(1024+a_dim, 512) → LN → ReLU → ×3 → Linear(512, 1024)` + residual connection
+- Reward: `Linear(1024+a_dim, 256) → ReLU → ×2 → Linear(256, 1)`
+- Ensemble diversity (weight cosine sim): ~0.60
+
+## 🏗️ How It Was Built
+
+1. Expert policies collect episodes in dm_control environments
+2. Each frame rendered at 224×224, encoded with V-JEPA 2 ViT-L (8-frame sliding windows)
+3. Dynamics ensemble trained with random data splits + different seeds
+4. Reward model trained to predict per-step rewards from z_t + a_t
+
+## 📈 Training Details
+
+- **GPU:** NVIDIA A100-SXM4-80GB (Prime Intellect)
+- **Total time:** 5.4 hours
+- **Total cost:** ~$7
+- **Dynamics val loss:** ~0.0008 (reacher, point_mass), ~0.0002 (cartpole)
+- **Temporal coherence:** >0.998 for all tasks
+
+## 🎯 Purpose
+
+These world models are designed for **"teach-by-showing"** — demonstrating a task via video,
+then using the learned dynamics + CEM planning to reproduce the shown behavior.