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ABot-N1 redefines urban navigation by achieving a 35% boost in point-of-interest arrival rates, setting new benchmarks for visual language navigation models.
Tactile dynamics are crucial for contact-rich manipulation, and VT-WAM outperforms existing models by 26.67% to 35.84% by effectively integrating visual and tactile cues.
Achieve robust robot manipulation across diverse viewpoints without camera calibration by synthesizing novel views with a geometry-aware video diffusion model.
Achieve 3x higher success in long-horizon robotic manipulation by explicitly separating high-level planning from low-level control, enabling memory-aware reasoning and adaptive replanning.
Robots can now manipulate objects with greater dexterity and adaptability thanks to a new world model that leverages both vision and high-frequency tactile feedback to predict and react to contact dynamics.
