SkipVLA：结合经典规划跳过VLA步骤实现快速机器人操作

中文摘要 AI 辅助

视觉-语言-动作（VLA）模型是一类通用机器人策略，将相机图像和语言指令直接映射为机器人动作。尽管前景广阔，这些模型在测试时仍然速度较慢，尤其是对于需要多次查询策略的长时域任务。近期工作通过蒸馏更小的模型、重叠异步动作块或将VLA与快速底层策略配对来降低VLA延迟，但仍需为整个任务运行学习到的策略。与VLA相反，经典运动规划器能快速找到无碰撞运动，但需要明确的目标且缺乏对任务的语义理解。在本工作中，我们提出SkipVLA，一种结合预训练VLA与经典运动规划器的混合策略，利用规划器处理自由空间运动，仅对抓取和放置等接触密集型技能查询VLA。SkipVLA重用VLA的冻结视觉-语言骨干网络来预测每个规划运动的目标位姿，并利用大型VLA已学到的知识来学习该预测器，无需向系统引入额外演示。我们在仿真中的13个LIBERO任务和物理6自由度YAM机械臂上的三个拾放任务上评估了SkipVLA，使用三种VLA，展示了高达2.5倍的任务完成速度提升和显著更低的能耗，同时保持相同的任务成功率。

英文摘要

Vision-Language-Action (VLA) models are a class of generalist robot policies that map camera images and language instructions directly to robot actions. While promising, these models remain slow at test time, particularly for long-horizon tasks that require many queries to the policy. Recent efforts reduce VLA latency by distilling smaller models, overlapping asynchronous action chunks, or pairing the VLA with a fast low-level policy, but still run a learned policy for the entire task. In contrast to VLA, classical motion planners quickly find collision-free motions, but require an explicit goal and have no semantic understanding of the task. In this work, we present SkipVLA, a hybrid policy that combines a pretrained VLA with a classical motion planner, using the planner for free-space motion and querying the VLA only for contact-rich skills such as grasping and placing. SkipVLA reuses the frozen vision-language backbone of the VLA to predict a target pose for each planned motion, and learns this predictor without additional demonstrations introduced into the system by using what was already learnt by the large VLA. We evaluate SkipVLA with three VLAs on 13 LIBERO tasks in simulation and three pick-and-place tasks on a physical 6-DoF YAM arm, demonstrating up to 2.5x faster task completion and significantly lower energy consumption while achieving the same task success rate.

发表机构

• Purdue University（普渡大学）

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