arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 2602.23648cs.RO
收藏

FAVLA:一种力适应的快速-慢速VLA模型用于接触丰富的机械臂操作

FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation

University of Science and Technology of China(中国科学技术大学);Xi'an Jiaotong University(西安交通大学);Central South University(中南大学)

Yao Li, Peiyuan Tang, Wuyang Zhang, Chengyang Zhu, Yifan Duan, Weikai Shi, Xiaodong Zhang, Zijiang Yang, Jianmin Ji, Yanyong Zhang

AI总结:

FAVLA通过解耦慢感知规划与快速接触感知控制,提升接触丰富任务中的反应性和成功率。

摘要:

力/扭矩反馈可以显著提升在接触丰富的操作中视觉-语言-动作(VLA)模型的性能,但大多数现有方法在单一操作频率上融合所有模态。这种设计忽略了真实机器人传感器不匹配的采样率,迫使对需要反应修正的高频接触提示进行下采样。结合常见的VLM-动作专家(AE)管道在昂贵的VLM更新之间执行动作块时主要开环,统一频率融合往往导致对冲击、粘滞滑动和力尖峰的响应延迟。我们提出了FAVLA,一种力适应的快速-慢速VLA,将慢感知规划与快速接触感知控制解耦。FAVLA以固定低频率运行慢VLM,以编码模态并生成潜在表示,以预测近未来的力变化。一个快速的AE则以可变的高频运行,根据最新力序列数据生成反应性动作。我们进一步引入了一个力适配器,将高频力特征注入到多个AE层中,并根据VLM预测的力变化自适应地调度AE的执行频率。在接触丰富的任务上的大量实验表明,FAVLA显著优于基线,实现了更优越的反应性和成功率,尤其是在操作期间接触力较小时。

英文摘要:

Force/torque feedback can substantially improve Vision-Language-Action (VLA) models on contact-rich manipulation, but most existing approaches fuse all modalities at a single operating frequency. This design ignores the mismatched sampling rates of real robot sensors, forcing downsampling of the high-frequency contact cues needed for reactive correction. Combined with common VLM-action-expert (AE) pipelines that execute action chunks largely open loop between expensive VLM updates, unified-frequency fusion often yields delayed responses to impacts, stick-slip, and force spikes. We propose FAVLA, a force-adaptive fast-slow VLA that decouples slow perception planning from fast contact-aware control. FAVLA runs a slow VLM at a fixed low frequency to encode modalities to produce latent representations and to predict near-future force variation. A fast AE then executes at a variable high frequency, conditioning on the latest force sequence data to generate reactive actions. We further introduce a force adapter that injects high-frequency force features into multiple AE layers, and adaptively schedules the AE's execution frequency based on the VLM's predicted force variation. Extensive experiments on contact-rich tasks demonstrate that FAVLA significantly outperforms baselines, achieving superior reactivity and success rates, especially with a smaller contact force during manipulation.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑