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Autoregressive video pretraining unlocks the full potential of long contexts for real-time robot control, boosting success rates by 15% and enabling complex manipulation previously impossible.
Failures in robotic manipulation are transformed into learnable, reusable capabilities, dramatically boosting task success and minimizing human intervention.
A single generalist model outperforms specialized systems, achieving over 35% improvement in real-world robotic task success.
SR-REAL's dual-path reasoning framework allows spatial VLMs to excel in both linguistic deduction and 3D geometric inference, significantly enhancing performance on complex spatial reasoning tasks.
SpatialClaw enables agents to dynamically compose and adapt their reasoning strategies, achieving a remarkable 11.2-point accuracy boost over traditional spatial agents.
