Advances in exoskeleton technology now enable interacting with rigid objects in a virtual or remote environment using one's hand and fingertips. However, interaction with non-solid materials - such as liquids, sediments and regolith - alongside solids, can greatly extend the versatility of this technology. Rendering rigid objects adequately requires a control loop with high update rates, whereas fluid dynamics equations are computationally expensive. To accommodate this, the fluid dynamics can be simplified - particularly for fluids with high viscosity - resulting in a fast-to-calculate model to enabling haptic rendering of viscous fluids and rigid bodies simultaneously using DLR's Exodex Adam hand exoskeleton. Viscosity as a proprioceptive cue of fluids can be presented to the human through force feedback at multiple points on the human hand - fingers and palm - letting the user interact with a virtual environment in a more natural way and making the experience more immersive. We carry out two user studies to investigate the human perception abilities of virtual fluids rendered with simplified dynamics, and the discernability of different viscosity in virtual fluids compared real fluids. Results show that virtual media can give the user the perception of interacting with a fluid, even with simplified models, at a high update frequency. Furthermore, the material discernibility corresponds well to actual interaction with real viscous fluids. This shows great promise forward for haptic in-hand interaction in fluid and mixed media environments.

Accurate real-time estimation of the pose and configuration of the human hand attached to a dexterous haptic input device is crucial to improve the interaction possibilities for teleoperation and in virtual and augmented reality. In this paper, we present an approach to reconstruct the pose of the human hand and the joint angles of the fingers when wearing a novel fixed-base (grounded) hand exoskeleton. Using a kinematic model of the human hand built from MRI data, we can reconstruct the hand pose and joint angles without sensors on the human hand, from attachment points on the first three fingers and the palm. We test the accuracy of our approach using motion capture as a ground truth. This reconstruction can be used to determine contact geometry and force-feedback from virtual or remote objects in virtual reality or teleoperation.