FG-CLTP: Fine-Grained Contrastive Language Tactile Pretraining for Robotic Manipulation

TL;DR

This paper introduces FG-CLTP, a novel framework for fine-grained tactile language pretraining in robotics, utilizing a large dataset and numerical tokenization to improve manipulation accuracy and generalization.

Contribution

The paper presents a new dataset, a discretized numerical tokenization method, and a 3D tactile-language-action architecture for enhanced robotic manipulation.

Findings

Achieved 95.9% classification accuracy

Reduced regression MAE by 52.6%

Minimal sim-to-real gap of 3.5%

Abstract

Recent advancements in integrating tactile sensing into vision-language-action (VLA) models have demonstrated transformative potential for robotic perception. However, existing tactile representations predominantly rely on qualitative descriptors (e.g., texture), neglecting quantitative contact states such as force magnitude, contact geometry, and principal axis orientation, which are indispensable for fine-grained manipulation. To bridge this gap, we propose FG-CLTP, a fine-grained contrastive language tactile pretraining framework. We first introduce a novel dataset comprising over 100k tactile 3D point cloud-language pairs that explicitly capture multidimensional contact states from the sensor's perspective. We then implement a discretized numerical tokenization mechanism to achieve quantitative-semantic alignment, effectively injecting explicit physical metrics into the multimodal feature space. The proposed FG-CLTP model yields a 95.9% classification accuracy and reduces the regression error (MAE) by 52.6% compared to state-of-the-art methods. Furthermore, the integration of 3D point cloud representations establishes a sensor-agnostic foundation with a minimal sim-to-real gap of 3.5%. Building upon this fine-grained representation, we develop a 3D tactile-language-action (3D-TLA) architecture driven by a flow matching policy to enable multimodal reasoning and control. Extensive experiments demonstrate that our framework significantly outperforms strong baselines in contact-rich manipulation tasks, providing a robust and generalizable foundation for tactile-language-action models.