GROOVE：VLA执行中基于几何引导的操作空间加加速度降低
GROOVE: Geometry-Guided Reduction of Operational-Space Jerk in VLA Execution
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中文总结 AI 辅助
GROOVE通过在线二次规划优化VLA执行中的命令块，降低操作空间加加速度，在LIBERO上任务成功率95.75%且加加速度大幅下降。
中文摘要 AI 辅助
分块视觉语言动作（VLA）策略每次查询会执行多个命令，但块内以及跨重新规划边界的加加速度会引起振荡运动和尖锐的致动器瞬态。我们提出GROOVE，一种在线调节器，它在原始三维末端执行器（EEF）路径周围搜索方向修正区域，无需重新训练或额外的VLA推理。它使用已执行的命令作为边界条件来优化新块，在每次命令后降低边界和块内加加速度，同时限制相对于原始计划的累积平移和局部轴角偏差。利用二次规划（QP），GROOVE生成一个立方体参考和十三个方向候选，然后在参考相对偏差上限下选择命令空间加加速度最低的一个。在保留的LIBERO基准上，GROOVE在评估方法中实现了最大的降低，分别将平移和旋转EEF加加速度降低了33.02%和43.42%，任务成功率为95.75%，而原始执行为93.75%。在50对具有实测执行时间的匹配UR5e上，它将平移和旋转工具中心点（TCP）加加速度分别降低了16.39%和19.49%，关节电流变化率降低了29.09%。
英文摘要
Chunked vision language action (VLA) policies execute several commands per query, but jerk within chunks and across replanning boundaries can induce oscillatory motion and sharp actuator transients. We present GROOVE, an online regulator that searches directional correction regions around the raw three dimensional end effector (EEF) path, without retraining or additional VLA inference. It optimizes the new chunk using delivered commands as boundary conditions, reducing boundary and within chunk jerk while bounding cumulative translation and local axis angle deviation from the raw plan after every command. Using quadratic programs (QPs), GROOVE generates a cube reference and thirteen directional candidates, then selects the one with the lowest command space jerk under a reference relative deviation cap. On a held out LIBERO benchmark, GROOVE achieves the largest reductions among the evaluated methods, reducing translational and rotational EEF jerk by 33.02% and 43.42%, respectively, with task success of 95.75% versus 93.75% for raw execution. Across 50 matched UR5e pairs with measured execution timing, it reduces translational and rotational tool center point (TCP) jerk by 16.39% and 19.49% and joint current slew by 29.09%.
发表机构
- POSTECH（浦项科技大学）
机构由 AI 辅助整理，请以论文原文为准。
