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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.
By encoding objects as local 3D meshes, FlowTouch achieves view-invariant visuo-tactile prediction that generalizes across sim-to-real and different sensor instances, a leap beyond camera-dependent mappings.
