Robotic manipulation in contact-rich environments remains challenging, particularly when relying on conventional tactile sensors that suffer from limited sensing range, reliability, and cost-effectiveness. In this work, we present LVTG, a low-cost visuo-tactile gripper designed for stable, robust, and efficient physical interaction. Unlike existing visuo-tactile sensors, LVTG enables more effective and stable grasping of larger and heavier everyday objects, thanks to its enhanced tactile sensing area and greater opening angle. Its surface skin is made of highly wear-resistant material, significantly improving durability and extending operational lifespan. The integration of vision and tactile feedback allows LVTG to provide rich, high-fidelity sensory data, facilitating reliable perception during complex manipulation tasks. Furthermore, LVTG features a modular design that supports rapid maintenance and replacement. To effectively fuse vision and touch, We adopt a CLIP-inspired contrastive learning objective to align tactile embeddings with their corresponding visual observations, enabling a shared cross-modal representation space for visuo-tactile perception. This alignment improves the performance of an Action Chunking Transformer (ACT) policy in contact-rich manipulation, leading to more efficient data collection and more effective policy learning. Compared to the original ACT method, the proposed LVTG with pretraining achieves significantly higher success rates in manipulation tasks.

翻译：在接触式丰富的环境中进行机器人操作仍然具有挑战性，尤其是在依赖传统触觉传感器时，这些传感器存在感知范围有限、可靠性不足和成本效益低的问题。本文提出LVTG，一种低成本视觉触觉抓取器，专为稳定、鲁棒和高效的物理交互而设计。与现有的视觉触觉传感器不同，LVTG凭借其增强的触觉感知区域和更大的张开角度，能够更有效且稳定地抓取更大、更重的日常物体。其表面皮肤采用高耐磨材料制成，显著提高了耐用性并延长了使用寿命。视觉与触觉反馈的集成使LVTG能够提供丰富、高保真的感官数据，从而在复杂操作任务中实现可靠的感知。此外，LVTG采用模块化设计，支持快速维护和更换。为了有效融合视觉与触觉，我们采用了一种受CLIP启发的对比学习目标，将触觉嵌入与其对应的视觉观测对齐，从而构建一个用于视觉触觉感知的共享跨模态表示空间。这种对齐提升了Action Chunking Transformer (ACT)策略在接触式丰富操作中的性能，实现了更高效的数据收集和更有效的策略学习。与原始ACT方法相比，所提出的基于预训练的LVTG在操作任务中取得了显著更高的成功率。