TactileReflex：基于噪声统计的视觉-触觉反射控制用于力敏感操作
TactileReflex: Noise-Statistics-Driven Vision-Tactile Reflex Control for Force-Sensitive Manipulation
AI总结：
针对易变形容器操作中的力控制难题，提出基于噪声统计的标定驱动反射控制范式，利用视觉触觉传感器提取图像级代理并实现约12Hz的优先反射通道，无需外部标定或手动调参。
摘要：
操作易变形的柔性容器（如装有液体的一次性塑料杯）需要在极窄的力裕度内实时调整抓取力：力不足会导致滑动，力过大则会使薄壁不可逆变形。现有方法难以完成此类力敏感操作任务。我们提出一种基于噪声统计的标定驱动反射控制范式，结合基于视觉的触觉感知：通过分析传感器的固有噪声特性（通过简短的静态保持-卸载协议），直接推导出所有控制器阈值，消除了外部力标定、试错手动调参或材料特定的物理模型。实现该范式，我们提出了TactileReflex，一个三通道闭环控制器，从双视觉触觉传感器中提取三个图像级代理：剪切强度（$S_y$）、接触强度（$F_n$）和压力中心（$C$），并以约12Hz驱动优先反射通道，用于滑动抑制、重量自适应释放和力保护。每个通道通过噪声导出的阈值直接在其代理上闭环。消融实验表明，只有完整的三通道系统能够防止容器不可逆变形（5/5成功，而部分配置最多1/5成功）。在动态倾倒任务中，固定力基线因姿态漂移在所有10次尝试中均失败，而TactileReflex在两种水量下实现了9/10成功。作为一个自包含且可解释的控制器，TactileReflex可作为高层操作流水线（包括无触觉VR遥操作和视觉-语言-动作策略）的即插即用安全层。
英文摘要：
Manipulating fragile deformable containers, such as disposable plastic cups filled with liquid, demands real-time grip-force adaptation within an extremely narrow force margin: insufficient force causes slip, while excessive force irreversibly deforms the thin wall. Existing approaches struggle to achieve such force-sensitive manipulation tasks. We propose a noise-statistics-based calibration-driven reflex control paradigm with vision-based tactile sensing: by analyzing the sensor's intrinsic noise characteristics (via a brief static-hold-and-unload protocol), we directly derive all controller thresholds, eliminating external force calibration, trial-and-error manual tuning, or material-specific physical models. Instantiating this paradigm, we present TactileReflex, a three-channel closed-loop controller that extracts three image-level proxies, shear intensity ($S_y$), contact intensity ($F_n$), and center of pressure ($C$), from dual visuo-tactile sensors and drives prioritized reflex channels at ~12 Hz for slip suppression, weight-adaptive release, and force protection. Each channel closes the loop directly on its proxy via noise-derived thresholds. Ablation demonstrates that only the full three-channel system is able to prevent irreversible container deformation (5/5 success vs. at most 1/5 for partial configurations). In a dynamic pouring task, fixed-effort baselines fail in all 10 attempts due to pose drift, while TactileReflex achieves 9/10 success across two water volumes. As a self-contained and interpretable controller, TactileReflex can serve as a plug-and-play safety layer beneath high-level manipulation pipelines, including haptic-free VR teleoperation and vision-language-action (VLA) policies.
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。
