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Gripper-aware models can dramatically improve robotic manipulation by tailoring strategies to specific gripper types, leading to better performance in diverse tasks.
Current robotic grasping methods struggle, with success rates under 70% in complex scenarios that demand reasoning and semantic understanding.
Model merging can drastically enhance continual learning for survival analysis, outperforming traditional methods while preserving privacy and reducing costs.
Adapting models at test time can significantly boost performance in continual learning for computational pathology, but task order matters more than you might think.
