Vision-Language-Action (VLA) policies are commonly trained from dense robot demonstration trajectories, often collected through teleoperation, by sampling every recorded frame as if it provided equally useful supervision. We argue that this convention creates a temporal supervision imbalance: long low-change segments dominate the training stream, while manipulation-critical transitions such as alignment, contact, grasping, and release appear only sparsely. We introduce FrameSkip, a data-layer frame selection framework that scores trajectory frames using action variation, visual-action coherence, task-progress priors, and gripper-transition preservation, then remaps training samples toward high-importance frames under a target retention ratio. Because FrameSkip operates only in the dataloader, it leaves the VLA architecture, action head, training objective, and inference procedure unchanged. Across RoboCasa-GR1, SimplerEnv, and LIBERO, FrameSkip improves the success-retention trade-off over full-frame training and simpler frame selection variants, achieving a macro-average success rate of 76.15% across the three benchmarks compared with 66.50% for full-frame training while using a compressed trajectory view that retains 20% of unique frames in the main setting.

翻译：摘要：视觉-语言-动作（Vision-Language-Action, VLA）策略通常从密集的机器人示教轨迹中训练，这些轨迹多通过遥操作收集，并采用采样每一记录帧的方式，仿佛每帧都提供同等有用的监督信号。我们认为这种惯例造成了时序监督不平衡：长段低变化序列主导训练流，而对操控至关重要的转换（如对齐、接触、抓取与释放）却仅稀疏出现。我们提出FrameSkip，一种数据层帧选择框架，通过动作变化、视觉-动作一致性、任务进度先验及夹爪状态转换保持性对轨迹帧进行评分，并在目标保留率下将训练样本重映射至高重要性帧。由于FrameSkip仅在数据加载器中运作，不改变VLA架构、动作头、训练目标及推理流程。在RoboCasa-GR1、SimplerEnv与LIBERO基准测试中，FrameSkip相比全帧训练及更简单的帧选择变体改善了成功保留权衡，在使用压缩轨迹视图（主设置中保留20%独特帧）的情况下，跨三个基准测试实现宏平均成功率为76.15%，而全帧训练为66.50%。