Dynamic Combination of Movement and Force for Softness Discrimination

Softness is an important source of information when interacting with real, remote, or virtual environments (VE) via a haptic human-machine-interface. Humans have no dedicated sense for perceiving softness; instead, inferring an object’s compliance haptically requires the combination of movement and force cues. A telepresence or VE system can alter an object’s mechanical impedance by artefacts such as time delay in the communication channel. Determining the limits for distortions caused by the technical system that do not affect the operator’s softness percept is crucial to ensure a realistic interaction experience. Characterising the perceptual system with a single performance measure such as the just noticeable difference (JND) neglects the role of active movement, which is known to influence perceptual performance. Overcoming this drawback, we propose the usage of dynamic models for haptic perception. On the example of an interaction with a soft virtual environment with time delay in the force feedback, we compare the prediction accuracy of different softness perception model candidates. Experimental data from three psychophysical experiments indicates that a dynamic state observer model captures the perceptual characteristics better than a time delay JND measure and an predictor base on an inverse model representation of the environment.