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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.
