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Technical University of Munich, University of Technology Nuremberg
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Current dual-arm manipulation policies falter in real-world applications, with significant challenges in early interactions and skill transfer from simulation.
Forget hand-engineered reward functions: Rewarding DINO learns dense, generalizable rewards for robot manipulation directly from visual data, opening the door to more autonomous skill acquisition.
