Haptic Stiffness Perception Using Hand Exoskeletons in Tactile Robotic Telemanipulation

TL;DR

The paper investigates stiffness perception in real-world telemanipulation using a hand exoskeleton on the operator side and tactile sensing on the robotic follower. It compares two haptic stiffness feedback methods: Method I renders force-proportional feedback $F_{L,j}^{1} = \alpha F_{F,j}$ with $\alpha = F_{L,\text{max}}/F_{F}^{\text{max}}$ and $F_{L,\text{max}} = 5$ N, while Method II adds displacement-based terms $F_{L,j}^{2} = \alpha F_{F,j} \Delta_Z \beta$ with $\Delta_Z$, $\beta$ defined as in the text. Ten naive participants performed ABX and S tasks with five samples spanning Shore hardness from ultra-soft to hard, under no-vision conditions; 24 ABX and 24 S trials were completed per method. Results show that subjects can discriminate stiffness with force feedback alone (mean around $75\%$ ABX; around $66\%$ S), with displacement feedback mainly aiding harder tasks or similar-stiffness cases, and that leader–follower kinematic mismatch affects performance, informing future feedback design.

Abstract

Robotic telemanipulation - the human-guided manipulation of remote objects - plays a pivotal role in several applications, from healthcare to operations in harsh environments. While visual feedback from cameras can provide valuable information to the human operator, haptic feedback is essential for accessing specific object properties that are difficult to be perceived by vision, such as stiffness. For the first time, we present a participant study demonstrating that operators can perceive the stiffness of remote objects during real-world telemanipulation with a dexterous robotic hand, when haptic feedback is generated from tactile sensing fingertips. Participants were tasked with squeezing soft objects by teleoperating a robotic hand, using two methods of haptic feedback: one based solely on the measured contact force, while the second also includes the squeezing displacement between the leader and follower devices. Our results demonstrate that operators are indeed capable of discriminating objects of different stiffness, relying on haptic feedback alone and without any visual feedback. Additionally, our findings suggest that the displacement feedback component may enhance discrimination with objects of similar stiffness.

Figures (6)

• Figure 1: The teleoperation setup connecting the Leader HGlove exoskeleton glove to the Allegro robotic hand. Forces measured from the robotic thumb, index, and ring fingers are rendered on the glove's thumb, index, and middle fingers, respectively.
• Figure 2: Samples of same-sized objects composed of soft materials such as gel and silicone. The samples are numbered from 1 to 5 as their stiffness increases.
• Figure 3: The graph compares the overall success rate for each task by method, with Task ABX results on the left and Task S results on the right, without differentiating between experimental days. $\blacklozenge$ are the outliers.
• Figure 4: Task success rates for both groups, subdivided by experimental days and methods applied.
• Figure 5: Spider plot showing Success Rate performance for Method I and II effectiveness in Task ABX for object pairs.