Article

Sensorless force/torque observer and hybrid pose-force control framework for physical human-robot interaction

The framework combines a neural-network-enhanced robust wrench observer with a hybrid pose-force controller for physical human-robot interaction (pHRI), enabling end-effector pose and force tracking without force/torque sensors. The neural network compensates for system friction and unmodeled dynamics to improve observer performance, and task-null-space decomposition separates joint velocities and reduces the effect of human disturbance on the observer. Experimental results show that the framework achieves robust and accurate wrench estimation while maintaining reliable pose and force tracking, and can be applied to various robotic arm platforms without force/torque sensors.

Part of the event: Proposal of a sensorless hybrid pose-force control framework for pHRI

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