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The latest AI City Challenge reveals that integrating foundation models with synthetic data design can significantly boost performance in complex urban scenarios.
Task-dependent action frames can enhance robotic manipulation performance, with MoF outperforming traditional single-frame policies in both simulation and real-world applications.
LingBot-VLA 2.0 showcases a remarkable leap in robotic manipulation, achieving strong cross-embodiment performance with enhanced predictive capabilities.
