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This work proposes EvoSkill-GUI, a training-free framework in which each skill is a structured multi-file package containing retrieval metadata, executable plans, backup localization, failure-recovery rules, accessibility utilities, and failure cases.
RePolicy achieves superior safety-policy invocation in language model agents, adapting dynamically to changing contexts and unseen trajectories.
Achieving robust robot skill generalization while maintaining critical motion geometry, SMP reduces dynamic violations and preserves end-effector paths during execution.
Finally, a unified open-source framework lets you train, evaluate, and deploy GUI agents across real devices and chat platforms, closing the gap between research and real-world application.
