Pith Number

pith:57MOY35R

pith:2024:57MOY35RKSWDA3PUOXJE5ICEAW

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

Gaoyue Zhou, Hengkai Pan, Lerrel Pinto, Yann LeCun

DINO-WM uses pre-trained DINOv2 patch features to build world models that support zero-shot planning from offline data.

arxiv:2411.04983 v2 · 2024-11-07 · cs.RO · cs.AI

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Claims

C1strongest claim

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.

C2weakest assumption

That predicting future DINOv2 patch features alone captures sufficient dynamics information to enable reliable planning without visual reconstruction or task-specific components.

C3one line summary

DINO-WM builds world models on pre-trained DINOv2 features to enable zero-shot planning from offline data without rewards or demonstrations.

References

131 extracted · 131 resolved · 27 Pith anchors

[1] Legged locomotion in challenging terrains using egocentric vision, 2022 2022

[2] Self-supervised learning from images with a joint-embedding predictive architecture 2023

[3] Nonlinear and adaptive control with applications, volume 187 2008

[4] V- JEPA : Latent video prediction for visual representation learning, 2024 2024

[5] RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control 2023 · arXiv:2307.15818

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Cited by

27 papers in Pith

stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation

Beyond Euclidean Proximity: Repairing Latent World Models with Horizon-Matched Trajectory Reachability Metrics

Efficient Agentic Reasoning Through Self-Regulated Simulative Planning

What Drives Success in Physical Planning with Joint-Embedding Predictive World Models?

Ada-Diffuser: Latent-Aware Adaptive Diffusion for Decision-Making

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First computed	2026-05-17T23:38:13.635938Z
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Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

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efd8ec6fb154ac306df475d24ea044059ae2ad452412b6a444c84a360b52da61

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arxiv: 2411.04983 · arxiv_version: 2411.04983v2 · doi: 10.48550/arxiv.2411.04983 · pith_short_12: 57MOY35RKSWD · pith_short_16: 57MOY35RKSWDA3PU · pith_short_8: 57MOY35R

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curl -sH 'Accept: application/ld+json' https://pith.science/pith/57MOY35RKSWDA3PUOXJE5ICEAW \
  | jq -c '.canonical_record' \
  | python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: efd8ec6fb154ac306df475d24ea044059ae2ad452412b6a444c84a360b52da61

Canonical record JSON

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