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FutureNav redefines VLN by simultaneously predicting actions and modeling world states, achieving unprecedented performance with a streamlined architecture.
MotionWAM achieves over 30% higher success rates in real-time humanoid manipulation tasks by unifying motion control across the entire body, challenging the effectiveness of traditional hierarchical models.
AffordanceVLA transforms robotic manipulation by using structured affordance cues to create precise perception-action mappings, outperforming traditional models.
Robots can now navigate complex social environments with unprecedented success thanks to a new world model that anticipates both scene changes and human behavior based on potential actions.
