让机器人双手感知受力并自动调节，稳定抓握不同重量物体
Multifingered force-aware control for humanoid robots
- 通过触觉传感器估计受力，动态调整躯干到手指的运动
- 在五种物体平衡任务中成功率82.7%，多物场景准确率达80%
- 不依赖特定传感器，适用于任何能估力的触觉系统
本文研究多指手型机器人平台的力感知控制与力分布问题。基于目标位置和触觉传感器的力估计值，设计控制器协同调节躯干、手臂、手腕及手指运动，实现对质量分布各异或接触不稳物体的稳定抓握。为估计力，我们使用五个Xela磁性传感器与压头交互采集触觉信号与真实力数据，并训练力估计算法。提出基于模型的控制策略，最小化接触压力中心（CoP）与指尖接触多边形质心之间的距离。由于方法依赖力估计而非原始触觉信号，可适配任意具备力估计能力的传感器。在五种物体平衡任务中取得82.7%的成功率，并在多物体场景下达到80%的准确率。代码与数据见：https://github.com/hsp-iit/multifingered-force-aware-control。
原文摘要 · Abstract (English)
In this paper, we address force-aware control and force distribution in robotic platforms with multi-fingered hands. Given a target goal and force estimates from tactile sensors, we design a controller that adapts the motion of the torso, arm, wrist, and fingers, redistributing forces to maintain stable contact with objects of varying mass distribution or unstable contacts. To estimate forces, we collect a dataset of tactile signals and ground-truth force measurements using five Xela magnetic sensors interacting with indenters, and train force estimators. We then introduce a model-based control scheme that minimizes the distance between the Center of Pressure (CoP) and the centroid of the fingertips contact polygon. Since our method relies on estimated forces rather than raw tactile signals, it has the potential to be applied to any sensor capable of force estimation. We validate our framework on a balancing task with five objects, achieving a $82.7\%$ success rate, and further evaluate it in multi-object scenarios, achieving $80\%$ accuracy. Code and data can be found here https://github.com/hsp-iit/multifingered-force-aware-control.
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