Multifingered force-aware control for humanoid robots
This work provides an incremental improvement in robust manipulation for humanoid robots by enabling more stable grasping and interaction with objects of varying properties.
This paper addresses force-aware control and force distribution for multi-fingered robot hands. The authors developed a controller that adapts robot motion to redistribute forces, maintaining stable contact with objects, achieving an 82.7% success rate in a balancing task with five objects and 80% accuracy in multi-object scenarios.
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.