AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This work introduces Video2Skill, a benchmark that covers robot tabletop manipulation and human kitchen activity and tests three core capabilities: locating manipulation events in time, grouping events of the same transformation, and deciding when to reuse an existing skill or create a new one.
Robotic learning takes place in dynamic environments with large behavior spaces. A terminal success signal only tells the robot whether the task is completed. It does not explain whether the current behavior is making progress, remaining unchanged, or undoing earlier progress. For this reason, recent studies have incre...
APT-Tune is proposed, a parameter-efficient recipe that teaches VLMs to use causal transitions without forgetting how to answer video questions, and substantially improves APT recall while also improving event-level video transfer.
From YAML-first specifications that decouple contents, placement, behavior, and agent exposure, MagicSim constructs diverse executable worlds spanning task families, interaction regimes, physics, layouts, sensors, avatars, and robot embodiments in one reset-and-step loop.
Experimental results demonstrate that Phys4D substantially improves fine-grained spatiotemporal and physical consistency compared to appearance-driven baselines, while maintaining strong generative performance.
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