Haptics

Figure 4. Haptic and visual rendering of dexterous manipulation using Phantom devices. From: Fu and Santello (2014). — **Figure 4.** Haptic and visual rendering of dexterous manipulation using Phantom devices. From: Fu and Santello (2014).

We have been using commercially-available haptic technologies to investigate the mechanisms underlying dexterous manipulation and explore scientific applications of customized tactile devices. Examples of these applications include the use of an impedance-based haptic interface (Phantom Premium) to test hypotheses on the role of vision¹ and sensorimotor learning of manipulation forces² (Fig. 4). We have also used a customized wearable haptic device³ to quantify the roles of tactile and non-tactile inputs for fingertip distance estimation^4,5 (Fig. 5).

Figure 5. Devices used to render haptic feedback associated with grasping an object. Phantom haptic devices were used to provide non-tactile feedback in response to forces generated by the subject. The wearable haptic devices were used to provide ta… — **Figure 5.** Devices used to render haptic feedback associated with grasping an object. Phantom haptic devices were used to provide non-tactile feedback in response to forces generated by the subject. The wearable haptic devices were used to provide tactile inputs to the finger pads matching the skin deformation associated with exertion of fingertip forces. From: Toma et al. (2019).

Our interest in exploring and testing customized haptic rendering solutions is motivated by (a) the ability of these devices to simulate realistic physical environments and (b) the goal of investigating the features of this technology that can elicit transparent and realistic interactions. This work has been developed in collaboration with robotics and haptics labs working on the development of innovative haptic technologies (Centro Piaggio at the University of Pisa; University of Siena; University of Aarhus)(Fig. 6). Examples of these collaborations include the characterization of fingertip-wearable tactile sensors⁶ and the validation and application of a 2-DoF tactile actuators.³

Figure 6. Electronics and mechanics of the 2-DoF tactile actuators described in Chinello et al., 2018 (under review). A: linear gear. B: stand for tracking. C: normal motor. D: finger stand. E: lateral motor. F: lateral platform and force sensor. — **Figure 6.** Electronics and mechanics of the 2-DoF tactile actuators described in Chinello et al., 2018 (under review). A: linear gear. B: stand for tracking. C: normal motor. D: finger stand. E: lateral motor. F: lateral platform and force sensor.

For more information on these projects contact: Marco Santello, Simone Toma

References

Fu Q. and Santello M. (2014). Coordination between digit forces and position: interaction between anticipatory and feedback control. The Journal of Neurophysiology.
Fu Q. and Santello M. (2015). Dexterous manipulation: Learning interference induces increase in effort. Soc. Neurosci. Abst. 803.15, 45th Meeting of the Society for Neuroscience, Chicago IL, USA: October 2015. Poster presentation.
Chinello F, Toma S, Piccoli-Gajek M., Shibata D., Santello M., Praticchizzo D. (2018). Design and evaluation of a novel two degrees of freedom wearable tactile system for normal and shear forces. IEEE Transaction on Hapitcs (under review).
Shibata D., Toma S., Chinello F., Santello M., Praticchizzo D. (2017). Tactile and non-tactile signals are linearly integrated for the estimation of fingertip distance. Poster presented at 27th meeting of the Society for the Neural Control Movement.
Toma S., Shibata D., Chinello F., Praticchizzo D., Santello M. (2018). Linear integration of tactile and non-tactile inputs mediates estimation of fingertip relative position. Frontiers in Neuroscience Perception Science (under review).
Battaglia E., Bianchi M., Altobelli A., Grioli G, Catalano M.G., Serio A., Santello M. and Bicchi A. (2015). ThimbleSense: a fingertip-wearable tactile sensor for grasp analysis. IEEE Transactions on Haptics.