Mu Xu

ABot-3DWorld 0 achieves state-of-the-art scene fidelity in 3D content creation, even outperforming established models like Marble with rich multimodal inputs.

ABot-N1 redefines urban navigation by achieving a 35% boost in point-of-interest arrival rates, setting new benchmarks for visual language navigation models.

A general Agent OS can boost long-horizon robotic execution and enable continual learning through structured memory management and self-evolution.

For the first time, a scaling law for quadruped motion tracking reveals that performance consistently improves with larger training datasets, unlocking new capabilities in robotic locomotion.

Agents can now explore environments more efficiently by thinking like humans, prioritizing key landmarks and semantic information during online memory construction.

Achieve stable, controllable, and semantically consistent long-form video generation by decoupling local dynamics from global semantic anchors.

Ditch discrete waypoints: VLA models can now generate smooth, physically plausible robot trajectories by directly regressing continuous action functions.

Autonomous driving gets a human-like reasoning boost: MindDriver uses progressive multimodal reasoning to bridge the gap between semantic understanding and physical trajectory planning.

Forget task-specific architectures: a single Vision-Language-Action foundation model, ABot-N0, now dominates embodied navigation across five distinct tasks.

By learning to project actions onto a low-dimensional manifold, ABot-M0 achieves faster and more stable robotic control policies compared to directly predicting actions in the full high-dimensional space.