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FutureSurf reveals that existing models can miss up to 4.1 times the expected accuracy in predicting future surfaces, challenging the assumptions of current dynamic scene reconstruction methods.
Persistent object tokens enable humanoid robots to achieve a 71/80 success rate in complex loco-manipulation tasks, significantly outpacing previous benchmarks.
Human-as-Humanoid achieves a staggering 4.8–7.2x increase in demonstration throughput, transforming how humanoid robots learn from human actions.
EgoPriMo enables humanoid robots to generate and forecast complex motions interactively using just egocentric observations and high-level language prompts.
Robots get a spatial-temporal reasoning boost with STARRY, a world model that aligns future predictions with action generation, leading to a significant jump in manipulation success.
