A psychophysical evaluation of haptic controllers: viscosity perception of soft environments

Bayesian framework for bilateral teleoperation systems over unreliable network

In this paper, we present a novel stochastic framework for network-based bilateral teleoperation systems. A Bayesian approach, which provides robust tracking performance in real-world applications, is proposed to estimate and predict the stochastic variables and compensate for the unreliable network conditions. Combining with a practical approach in transport and application layers of the Internet, this paper demonstrates a high performance and efficient prediction and estimation method for bilateral teleoperation system. Experimental results show that the proposed Bayesian approach estimates and predicts true position and force data over unreliable network conditions, and therefore, improves the performance of overall teleoperation systems.

Tactual perception of liquid material properties

In this paper, studies into the tactual perception of two liquid material properties, viscosity and wetness, are reviewed. These properties are very relevant in the context of interaction with liquids, both real, such as cosmetics or food products, and simulated, as in virtual reality or teleoperation. Both properties have been the subject of psychophysical characterisation in terms of magnitude estimation experiments and discrimination experiments, which are discussed. For viscosity, both oral and manual perception is discussed, as well as the perception of the viscosity of a mechanical system. For wetness, the relevant cues are identified and factors affecting perception are discussed. Finally, some conclusions are drawn pertaining to both properties.