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This work evaluates cross-embodiment transfer on RoboTwin 2.0 in a controlled setup, two bimanual robots demonstrate disjoint task sets, a policy is trained on all the demonstrations, and each robot is evaluated closed-loop on the tasks only the other demonstrated.
Temporally centered SIGReg boosts multi-task learning performance by 1.7x, revealing critical incompatibilities in latent representation structures.
