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UC Berkeley

UC Berkeley is a leading academic institution contributing to world model research, reinforcement learning, and embodied AI.

robotics model-based-rl simulation embodied-ai

About UC Berkeley

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UC Berkeley's AI research groups have made foundational contributions to model-based reinforcement learning, robotics, and world models. Key labs include Berkeley AI Research (BAIR) and the Robot Learning Lab, with work spanning visual model-based RL and sim-to-real transfer.

UC Berkeley is a academic organization based in United States with a visible footprint in AI world models.

Lab Snapshot

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AttributeValue
LabUC Berkeley
Short NameUC Berkeley
Typeacademic
RegionUnited States
Related Models2
DescriptionUC Berkeley's AI research groups have made foundational contributions to model-based reinforcement learning, robotics, and world models. Key labs include Berkeley AI Research (BAIR) and the Robot Learning Lab, with work spanning visual model-based RL and sim-to-real transfer.

Focus Areas

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model-based-rlroboticsvisual-rlsim-to-real

Related Models

Models currently associated with this lab in the local knowledge base.

ModelCategoryYearIndex v1.1
Large World Model (LWM)Foundation World Model202455/100

Quick Answer

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  • UC Berkeley is a academic organization based in United States with a visible footprint in AI world models.
  • On world-models.io, it is associated with 2 world models across editorially linked pages.
  • Use this page to review the organization profile, connected models, related comparisons, and official sources.

Editorial Trust Signals

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Published by world-models.io editorial board.

Lead editor Bernard Grenat.

This lab page distinguishes official organization sources from world-models.io editorial synthesis and related model coverage.

Each editorial page is assembled from primary sources, normalized into extractable summaries, checked for factual drift, and reviewed before publication or major refreshes. Last reviewed: 2026-06-21.

Pages are refreshed when a new paper, benchmark, release, architecture update, or stronger primary source materially changes the answer a reader or AI system should retrieve.

Each page links back to relevant primary sources and keeps a stable canonical URL so readers can verify claims, trace context, and reference the most up-to-date version. See the editorial policy.

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Official Sources

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