Vibrotactile Feedback Enhancement for Polishing Tasks: A Perceptual Study of Signal Deformation Strategies

In the teleoperation of skilled manual tasks such as precision polishing, haptic feedback with a high degree of fidelity is considered essential. However, matching the dynamic characteristics of the leader and follower systems is challenging, often degrading feedback quality. This degradation can impair demonstration data and reduce learning performance for both machine learning systems and human users. Conventional solutions have relied on complex hardware or control schemes, increasing cost and limiting applicability. Several studies have explored improving haptic perceptual quality via minimal- cost vibrotactile signal processing, deforming or exaggerating the original haptic signal. To examine the effects of different processing strategies, we compared four vibrotactile defor- mation methods: perceptual low-pass filtering, frequency-bin amplitude change emphasis, envelope-based nonlinear gain, and task-specific band-pass filtering emphasizing vibration features identified from real polishing data. Pre-recorded vi- brations were processed offline and presented to participants using a custom mock polisher with two voice-coil actuators. A perceptual evaluation revealed no significant differences in switch response behavior to vibration changes, indicating contact and pressing of the metal surface. In contrast, the task- specific band-pass filtering condition yielded significantly higher clarity, naturalness, and confidence scores than the others. The main contributions of this work are the development of a low-cost, medium-fidelity metal mock polisher and a task- specific vibration frequency deformation strategy for polishing, which can improve the perceived clarity of pressing sensations compared to both the strategies drawn from prior work and all other strategies tested here.