"DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning"

Below podcast on this paper is generated with Google's Illuminate.

https://arxiv.org/abs/2411.04983

The challenge lies in creating versatile world models for physical reasoning. Current models are often task-specific and need continuous online learning. This limits their ability to generalize and adapt to new situations. This paper introduces a method to address these limitations.

This paper proposes DINO World Model (DINO-WM). DINO-WM learns visual dynamics without reconstructing visuals directly. It uses pre-trained DINOv2 features. This enables offline training and task-agnostic planning.

-----

📌 DINO World Model leverages pre-trained DINOv2 patch features. This avoids explicit pixel reconstruction. Latent space dynamics modeling improves learning efficiency and task generalization.

📌 Causal attention in the Vision Transformer based transition model is crucial. It enforces temporal consistency during prediction. Frame level prediction using patch vectors effectively captures global dynamics.

📌 Zero-shot planning is achieved due to accurate latent predictions. Model Predictive Control directly operates in the learned latent space. This optimizes actions to achieve visual goals without retraining.

----------

Methods Explored in this Paper 🔧:

→ DINO World Model (DINO-WM) leverages pre-trained DINOv2 patch features as visual input.

→ It uses a modified Vision Transformer (ViT) architecture as its core transition model.

→ The model predicts future latent states by conditioning on past latent states and actions.

→ A causal attention mechanism is integrated to ensure temporal consistency in predictions.

→ Training occurs entirely in the latent feature space, avoiding pixel-level reconstruction.

→ An optional decoder, trained separately, allows for visualizing model predictions.

-----

Key Insights 💡:

→ Pre-trained visual features, specifically from DINOv2, are highly effective for learning robust world models.

→ Modeling dynamics in the latent space is more efficient than pixel reconstruction and focuses on task-relevant information.

→ Patch-based representations from DINOv2 are crucial for capturing spatial details needed in manipulation tasks.

→ DINO-WM enables zero-shot behavioral solutions and task-agnostic planning at test time.

→ The model exhibits strong generalization to unseen environment configurations and object variations.

-----

Results 📊:

→ Achieves a 0.98 Success Rate on the Maze navigation task and a 0.90 Success Rate on the complex Push-T manipulation task.

→ Outperforms other world models in LPIPS metric, reaching 0.007 on PushT and 0.0016 on Wall environments.

→ Demonstrates strong generalization capabilities, achieving 0.82 Success Rate on WallRandom and 0.34 Success Rate on PushObj environments.