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SEAM Smooths Vision-Language-Action Policy Execution, Cutting Jerk by 28%SEAM方法平滑VLA策略执行，边界抖动降低28%

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Researchers propose SEAM (Smooth Execution of Action-Chunked Motion), a training-free inference-time method to fix multimodal bifurcation in Vision-Language-Action (VLA) policies where independently generated action chunks produce abrupt discontinuities at boundaries.
Using Velocity-guided Loss Steering (VLS), SEAM derives consistent trajectory targets from previously executed chunks and applies closed-form corrections after each denoising step, reducing boundary jerk by 28% on LIBERO-10 benchmark with pi_0.5 model.
The method achieves smoother execution without backpropagation through the policy network, rejection sampling, or retraining, enabling efficient real-time robotic control.

研究人员提出SEAM（行动分块运动平滑执行）方法，一种推理阶段的训练无关方法，用于修复视觉语言行动（VLA）策略中的多模态分叉问题，即独立生成的行动块在边界处造成的突然不连续。
SEAM采用速度引导损失转向（VLS）技术从已执行块导出一致的轨迹目标，在每个欧拉步后应用闭式修正，使LIBERO-10基准测试中pi_0.5模型的边界抖动降低28%。
该方法无需通过策略网络反向传播、拒绝采样或重新训练即可实现平滑执行，为实时机器人控制提供计算效率。

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