论文arXiv 2611.07235今天

自适应混合力视控制实现实验室挤压瓶精准液体分配

针对实验室可变形挤压瓶操作难题，提出自适应混合力视控制器。结合EMG信号生成力参考，在12种工况下验证，液体分配MAE降至6.54mL，较纯力控提升59%，有效解决柔性物体形变恢复导致的操控误差。

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本文由 AI 根据公开内容整理，可能有误，请以原文为准：Adaptive Hybrid Force-Vision Control with EMG-Derived References for Deformable Bottle Manipulation（arXiv 2611.07235）

核心内容

针对实验室自动化中可变形挤压瓶液体分配难题，研究提出一种自适应混合视觉-力控制器。该方法通过肌电图（EMG）采集人类演示数据并映射为期望抓握力作为参考基准，根据交互状态动态融合力与视觉反馈，以应对瓶体形变与回弹特性。实验覆盖2种目标体积、2种瓶型及1至3次挤压共12种组合条件，验证了该策略在复杂柔性操作中的有效性。

系统核心在于依据交互状态自适应切换力控与视觉反馈权重，克服纯力控无法感知形变恢复的缺陷。对比测试显示，混合控制器液体分配平均绝对误差（MAE）仅为6.54 mL，显著优于纯力控制器的15.92 mL，精度提升约59%。同时，基于EMG导出的力参考使机器人动作轨迹更贴近人类自然操作模式，证明了生物信号引导在柔性物体精细操控中的工程价值。

关键看点

控制架构：采用自适应混合视觉-力控制策略，依据交互状态动态融合多模态反馈，解决柔性瓶体形变与弹性恢复导致的操控失稳问题
精度指标：在12种挤压瓶操作工况测试中，液体分配平均绝对误差（MAE）达6.54 mL，较传统纯力控制器（15.92 mL）降低59%
人机映射：利用肌电图（EMG）传感采集人类演示数据并映射为期望抓握力，作为控制器力参考基准，显著提升动作复现的自然度与准确性
验证场景：实验涵盖2种目标出液量、2种塑料挤压瓶及1至3次挤压动作组合，系统性验证了算法在不同形变程度下的鲁棒性

工程与产业观察

该研究为具身智能处理非刚性物体提供了「生物信号引导+多模态自适应融合」的工程范式。EMG作为力控先验有效弥补了纯视觉或纯力觉在柔性操作中的盲区，MAE从15.92mL降至6.54mL的量化结果证实了混合控制在实验室自动化等精密场景的落地潜力，对柔性抓取算法设计具有直接参考价值。

英文摘要 (Abstract)

Robotic automation of laboratory tasks has become a growing interest to remove researchers from hazardous environments and repetitive procedures. However, even equipment as simple as squeeze bottles poses a challenging manipulation problem due to its deformability. This paper presents an adaptive hybrid vision-force controller for robotic grippers to effectively manipulate laboratory squeeze bottles to dispense liquid. This approach integrates force and vision feedback according to the interaction state, enabling effective squeezing while accounting for bottle deformation and recovery. The proposed controller is validated by comparing its performance with a pure force controller on 12 combinations of conditions consisting of 2 target dispense volumes, 2 plastic squeeze bottles, and 1 to 3 squeezes used to dispense. Human demonstrations of these operating conditions were collected using electromyography (EMG) sensing mapped to a desired grip force, which was then used as the force reference for the controllers to squeeze the bottles. Results demonstrated that the proposed adaptive hybrid vision-force controller achieved more accurate liquid dispensing with a mean absolute error (MAE) of 6.54 mL compared to a MAE of 15.92 mL from the pure force controller, while also replicating the human action recorded from the EMG signal more closely.

作者：Jeffrey Lee, Geoffrey Hanks, Evangeline Bouganim, Yue Hu