Pose-Aware Modeling to Mitigate Pose-Related Artifacts in Tactile Gloves

2026-07-24

AI Summary

This work addresses pose-related artifacts (PRAs)—spurious signals unrelated to contact forces—that arise in flexible tactile gloves during hand posture changes, leading to false or delayed contact detection at low-force regimes and elevating the minimum detectable force (MDF). The study presents the first systematic characterization of the relationship between hand pose and these artifacts and introduces a hardware-agnostic, glove-independent pose-aware residual correction framework. By incorporating hand pose information, the method employs a dedicated residual prediction branch to explicitly compensate tactile signals. Evaluated across three distinct glove designs and fifteen users, the approach consistently reduces MDF by 10.4%, 12.2%, and 18.3%, respectively, while improving all relevant performance metrics, thereby demonstrating its generalizability and effectiveness.

Abstract

Tactile gloves digitize contact and force during hand-object interactions, enabling robotics applications in dexterous manipulation, teleoperation, and learning from demonstration. To preserve hand dexterity and capture the nuances of natural interactions, these gloves and the integrated tactile sensors are designed to be soft, flexible, and comfortable. However, such flexible sensors are sensitive not only to contact forces but also unavoidably to hand pose changes, resulting in pose-related artifacts (PRAs). PRAs are especially problematic in the low-force range, resulting in misdetections or late-onset detections of contact, which raises the minimum detectable force (MDF) of the glove. In this work, we characterize the PRAs in relation to pose and force. Building on these insights, we introduce a glove-agnostic algorithmic framework that leverages hand pose information, which is increasingly available, to mitigate PRAs without glove modifications. Our pose-aware force estimation model augments tactile-to-force pipelines with a residual prediction branch that explicitly accounts for pose-induced sensor deformations. We validate our approach across 3 glove designs and 15 users, reducing MDF by 10.4%, 12.2%, and 18.3%, with consistent improvements across all evaluated metrics. This method provides a practical path to improving the usability of tactile gloves in data collection and diverse robotic applications.

Problem

• tactile gloves
• pose-related artifacts
• minimum detectable force
• hand pose
• sensor deformation

Innovation

• pose-aware modeling
• tactile gloves
• pose-related artifacts
• force estimation
• residual prediction