用几何模型统一处理触觉误差，让机器人稳定抓取和操作物体
GeoDEx: A Unified Geometric Framework for Tactile Dexterous and Extrinsic Manipulation under Force Uncertainty
- 基于平面、锥体等几何体建模触觉力反馈
- 在力读数有噪声时仍能成功抓取与操作物体
- 比传统优化方法快14倍，适合实时控制场景
触觉感知使机器人能够检测接触并测量交互力，从而完成抓取易碎物或使用工具等挑战性任务。理论上，触觉传感器可赋予机器人此类能力，但其测得的力值因校准难题和噪声影响，精度远低于专用力传感器，限制了其在需力控的应用中价值。本文提出GeoDEx，一个统一的估计、规划与控制框架，利用平面、圆锥、椭球等几何原语，在力读数不确定条件下实现灵巧与外在操作。实验表明，直接依赖不准确且嘈杂的触觉力读数会导致操作不稳定或失败，而本方法能成功完成多种物体的抓取与外在操作；相较于直接使用SOCP（二阶锥规划）进行优化，本框架在规划与力估计上实现14倍加速。
原文摘要 · Abstract (English)
Sense of touch that allows robots to detect contact and measure interaction forces enables them to perform challenging tasks such as grasping fragile objects or using tools. Tactile sensors in theory can equip the robots with such capabilities. However, accuracy of the measured forces is not on a par with those of the force sensors due to the potential calibration challenges and noise. This has limited the values these sensors can offer in manipulation applications that require force control. In this paper, we introduce GeoDEx, a unified estimation, planning, and control framework using geometric primitives such as plane, cone and ellipsoid, which enables dexterous as well as extrinsic manipulation in the presence of uncertain force readings. Through various experimental results, we show that while relying on direct inaccurate and noisy force readings from tactile sensors results in unstable or failed manipulation, our method enables successful grasping and extrinsic manipulation of different objects. Additionally, compared to directly running optimization using SOCP (Second Order Cone Programming), planning and force estimation using our framework achieves a 14x speed-up.
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