HyperTaxel: Hyper-Resolution for Taxel-Based Tactile Signals Through Contrastive Learning
This work addresses a domain-specific problem for robotics by improving tactile signal processing, though it appears incremental as it builds on existing contrastive learning methods.
The paper tackles the problem of low spatial-resolution in taxel-based tactile signals for robots by proposing HyperTaxel, a framework that uses contrastive learning to map sparse signals to high-resolution contact surfaces, resulting in improved performance over baselines and enhanced downstream tasks like surface classification and pose estimation.
To achieve dexterity comparable to that of humans, robots must intelligently process tactile sensor data. Taxel-based tactile signals often have low spatial-resolution, with non-standardized representations. In this paper, we propose a novel framework, HyperTaxel, for learning a geometrically-informed representation of taxel-based tactile signals to address challenges associated with their spatial resolution. We use this representation and a contrastive learning objective to encode and map sparse low-resolution taxel signals to high-resolution contact surfaces. To address the uncertainty inherent in these signals, we leverage joint probability distributions across multiple simultaneous contacts to improve taxel hyper-resolution. We evaluate our representation by comparing it with two baselines and present results that suggest our representation outperforms the baselines. Furthermore, we present qualitative results that demonstrate the learned representation captures the geometric features of the contact surface, such as flatness, curvature, and edges, and generalizes across different objects and sensor configurations. Moreover, we present results that suggest our representation improves the performance of various downstream tasks, such as surface classification, 6D in-hand pose estimation, and sim-to-real transfer.