Testing of an assistive robot system for haptic exploration of objects

INTRODUCTION

When children with physical impairments cannot perform hand movements for haptic exploration, they may miss opportunities to learn the properties of objects. Assistive robots may enable them to make manipulation actions.

OBJECTIVE

To examine the differences between using a robotic teleoperation system with haptic feedback and manual exploration when making perceptual comparisons about object properties. Accuracy and exploratory procedures (EP) using the system were compared to those in manual exploration.

METHOD

Twenty adults without physical disabilities and ten typically developing children manipulated four pairs of objects and chose one based on size, roughness, hardness and shape. All participants completed the task with the robotic system (Tech) and manual exploration (No Tech), with the order counterbalanced.

RESULTS AND CONCLUSION

Participants performed a previously unidentified EP, "tapping", in the Tech condition. Enclosure was not possible with the robot end effector, but tapping afforded the required perceptual information. Adults' perceptual comparisons were always accurate and they predominantly performed the optimum EP in both conditions. Even when children performed the optimum EP with the system, their answers were less accurate than with manual exploration. Most gave the correct answer, except for hardness, which was likely due to mechanical flexibility in the robotic system.

Pseudo-Haptic Feedback in Teleoperation

In this paper, we develop possible realizations of pseudo-haptic feedback in teleoperation systems based on existing works for pseudo-haptic feedback in virtual reality and the intended applications. We derive four potential factors affecting the performance of haptic feedback (calculation operator, maximum displacement, offset force, and scaling factor), which are analyzed in three compliance identification experiments. First, we analyze the principle usability of pseudo-haptic feedback by comparing information transfer measures for teleoperation and direct interaction. Pseudo-haptic interaction yields well above-chance performance, while direct interaction performs almost perfectly. In order to optimize pseudo-haptic feedback, in the second study we perform a full-factorial experimental design with 36 subjects performing 6,480 trials with 36 different treatments. Information transfer ranges from 0.68 bit to 1.72 bit in a task with a theoretical maximum of 2.6 bit, with a predominant effect of the calculation operator and a minor effect of the maximum displacement. In a third study, short- and long-term learning effects are analyzed. Learning effects regarding the performance of pseudo-haptic feedback cannot be observed for single-day experiments. Tests over 10 days show a maximum increase in information transfer of 0.8 bit. The results show the feasibility of pseudo-haptic feedback for teleoperation and can be used as design basis for task-specific systems.