RobotScale: A Framework for Adaptable Estimation of Static and Dynamic Object Properties with Object-dependent Sensitivity Tuning

Marko Pavlic, Timo Markert, Sebastian Matich, Darius Burschka

Abstract: We propose a framework for the measurement of static and dynamic physical properties of manipulation objects using both robotic tactile and kinesthetic sensing - in particular data from fingertip force/torque (F/T) and robot joint torque sensors. It completes the manipulation-relevant information about new objects that cannot be estimated from a passive camera observation. The system allows to balance the accuracy and complexity of the estimation system against the costs and complexity of the approach. We evaluate methods that allow improving robustness against noise and model errors in the manipulation system used for the estimation. The approach is validated on experimental results using data from a torque-controlled robot manipulator and precision F/T sensors.