---
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.