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SWAM achieves superior navigation performance by seamlessly integrating observation and action generation, significantly enhancing efficiency and accuracy in embodied tasks.
AutoMine outperforms existing methods in scenario mining, achieving a HOTA-Temporal score of 36.38 in a competitive setting.
History-guided control in DFP allows for adaptable motion planning that avoids the pitfalls of static pattern copying, leading to safer and more stable driving trajectories.
Ditch the clunky text-based reasoning: LaST-VLA achieves new benchmarks in autonomous driving by thinking in a physically-grounded, latent spatio-temporal space.
