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Achieving a 52.5% success rate in real-robot manipulation tasks, GaussianDream++ redefines efficiency in 3D world modeling without the need for online Gaussian decoding.
Training-time Gaussian distillation can elevate WAM performance by over 19% by effectively integrating geometric and semantic information without altering deployment architecture.
Policies may succeed in tasks but still violate deformation tolerances, revealing a critical gap in current evaluation methods for deformable-object manipulation.
RoboDojo reveals that integrating simulation and real-world tasks can significantly enhance the evaluation of robot manipulation policies, bridging the gap between theoretical performance and practical deployment.
