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The Hong Kong University of Science and Technology
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Robots can adapt to unseen tasks purely through simulated imagination, boosting real-world manipulation success from 20% to 77% without requiring new human demonstrations or external environment rollouts.
Robot policies can now test and refine manipulation plans entirely within a learned visual simulator across ten distinct embodiments, gaining up to 21.4 percentage points in task success without any target-domain fine-tuning.
Overcoming the 2D-to-4D spatial bottleneck in VLMs does not require native 3D architectures; factorizing visual projections into verifiable planar, depth, and temporal RL objectives delivers immediate 4-6% benchmark gains.
