AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work evaluates cross-embodiment transfer on RoboTwin 2.0 in a controlled setup, two bimanual robots demonstrate disjoint task sets, a policy is trained on all the demonstrations, and each robot is evaluated closed-loop on the tasks only the other demonstrated.
DREAM is presented, a framework that generates fine-tuning data for a pretrained VLA from a captured workspace and a language instruction, without requiring a task-specific human demonstration, and whether it can serve as a scalable data-collection system for the deployment workspace.
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