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A unified policy can achieve near-perfect performance across diverse robotic embodiments without requiring extensive fine-tuning for specific tasks.
Future-aware geometry tokens enable autonomous vehicles to make safer and more efficient driving decisions, outperforming existing models.
Latent reasoning can beat explicit Chain-of-Thought – but only if you force it to learn causal dynamics via a visual world model, not just language.
Image-goal navigation gets a boost from hierarchical reasoning, using vision-language models for high-level planning and online RL for low-level execution, significantly reducing wandering and improving success in complex environments.
Compact student models can now achieve near EEG foundation model performance with significantly reduced model size and inference cost, thanks to a novel knowledge distillation framework.
Ditch language descriptions: this new driving model leverages dense 3D geometry for superior autonomous driving performance and cross-camera generalization.
