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Queen’s University, Canada
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Achieving near-random watermark removal accuracy without sacrificing image quality, MarkNull challenges the robustness of current digital watermarking techniques.
Achieving over 95% success in real-world robotic tasks after just 1.5 hours of training, this model-agnostic framework could redefine the deployment of VLA models in industry.
Achieve near-perfect sim-to-real transfer in robotic manipulation by closing the loop between synthetic data generation and robust policy training.
