DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning (2411.04983v2)

Abstract: The ability to predict future outcomes given control actions is fundamental for physical reasoning. However, such predictive models, often called world models, remains challenging to learn and are typically developed for task-specific solutions with online policy learning. To unlock world models' true potential, we argue that they should 1) be trainable on offline, pre-collected trajectories, 2) support test-time behavior optimization, and 3) facilitate task-agnostic reasoning. To this end, we present DINO World Model (DINO-WM), a new method to model visual dynamics without reconstructing the visual world. DINO-WM leverages spatial patch features pre-trained with DINOv2, enabling it to learn from offline behavioral trajectories by predicting future patch features. This allows DINO-WM to achieve observational goals through action sequence optimization, facilitating task-agnostic planning by treating goal features as prediction targets. We demonstrate that DINO-WM achieves zero-shot behavioral solutions at test time on six environments without expert demonstrations, reward modeling, or pre-learned inverse models, outperforming prior state-of-the-art work across diverse task families such as arbitrarily configured mazes, push manipulation with varied object shapes, and multi-particle scenarios.

• The paper introduces DINO-WM, a framework that uses DINOv2 pre-trained visual features for zero-shot planning in robotics.
• The methodology replaces pixel-level reconstruction with latent ViT-based state prediction, resulting in a 56% LPIPS improvement and a 45% increase in task success rates.
• The results demonstrate robust generalization across diverse robotic tasks, paving the way for more adaptable and cost-effective real-time planning solutions.

DINO-WM: Advancing Zero-Shot Planning in Robotics with Pre-Trained Visual Features

The paper presents DINO-WM, a methodological advancement in the field of world models, concentrating on zero-shot planning for robotic tasks via pre-trained visual features. The authors investigate a burgeoning area in robotics: the deployment of task-agnostic world models trained on offline datasets, seeking to address the limitations inherent in traditional model architectures.

Methodological Framework

DINO-WM leverages the expressiveness of DINOv2's pre-trained spatial patch features to model environmental dynamics without necessitating exhaustive image reconstruction. This approach contrasts with previous methods predominantly reliant on image reconstruction, which incurs significant computational overhead and frequently necessitates domain-specific reward information. DINO-WM's novel approach prioritizes the encoding of high-dimensional spatial and semantic features into a latent space. Herein, the ViT (Vision Transformer) architecture predicts future states, enabling task execution as an inference-time optimization problem centered on visual goal-reaching, distinct from traditional reward-guided frameworks.

Experimental Design

The efficacy of DINO-WM is empirically validated across a gamut of domains, spanning maze navigation, robotic pushing, and intricate particle manipulation. Among the notable results, the model demonstrated a 56% improvement in state-of-the-art Long-term Perceptual Image Patch Similarity (LPIPS) metrics and a 45% increase in success rates for arbitrary goal-reaching tasks when benchmarked against established world model frameworks. Importantly, DINO-WM achieves robust performance without reliance on auxiliary demonstrations or pre-learned inverse models, positioning it as a versatile tool for real-time planning in unfamiliar environments.

Contributions to the Field

The principal innovation of DINO-WM is its capacity to generalize across diverse environments with minimal task-specific heuristics. This adaptability is inherently valuable in robotics, offering pathways to more flexible deployment strategies where agents can be rapidly adapted to new tasks without retraining. Therein lies the prospect of significant reductions in operational costs and time investments.

Moreover, by functioning primarily in a latent space, DINO-WM circumvents the computational complexity customary to pixel-level predictions, offering an effective solution for the perceptual load encountered in real-time applications. This decoupling aligns with the contemporary shift towards leveraging large-scale, pre-trained models for foundational vision tasks, circumventing the necessity for extensive in-situ data collection.

Implications and Future Directions

The adoption of DINO-WM heralds a shift in embodied AI, particularly within domains where visual dynamics model fidelity directly correlates to task success. The success rates reported, though significant, illuminate avenues for further optimization, particularly in the exploration of hierarchical planning that combines high-level reasoning with fine-grained control.

However, challenges remain. The dependency on action data constraints the approach's applicability in scenarios devoid of action labels. Thus, the extension to unsupervised or weakly supervised settings could broaden the utility of the model. Furthermore, the reliance on a fixed latent representation model may face difficulties with evolving dynamic environments.

As AI continues to permeate multidisciplinary applications, the advancements represented by DINO-WM provide a springboard for future work on integrating large pre-trained models into control loops, not only for robotics but also for broader AI-driven industries. Collaborative endeavors that blend computer vision, control theory, and machine learning could pave the way for the next generation of adaptive, intelligent machines capable of seamless interactions within diverse environments.