Survey on World Models for Robot Learning Accepted by IJRR, Co-authored by Berkeley, Stanford and More
HirokatuKataoka · x · 2026-08-28
A comprehensive survey, "World Model for Robot Learning: A Comprehensive Survey", has been accepted by robotics journal IJRR. Authors span NTU, UC Berkeley, Stanford, UTokyo, Oxford, Microsoft, ETH Zurich, Princeton and Harvard.
The policy-centric survey covers how predictive world models serve robot learning:
- World model for policy: architectural paradigms coupling future prediction with action generation
- World model as simulator: learned environments for RL, validation, and decision-time evaluation
- Robotic video world models: video-based future prediction for imagination, controllable rollouts, and data amplification
The authors note two converging tracks: world models are increasingly integrated with policy generation while evolving toward controllable, structured, foundation-scale formulations. The accompanying timeline is maintained through March 31, 2026.
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