TransDex：基于点云重建的视觉-触觉政策预训练用于透明物体的精细操作
TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objects
AI总结：
TransDex通过点云重建预训练实现视觉-触觉融合政策，解决透明物体操作中的自遮挡和深度噪声问题，提升真实机器人系统中精细操作性能。
摘要：
精细操作能够完成复杂任务，但在操作透明物体时面临自遮挡、严重深度噪声和深度信息丢失的问题。为解决此问题，本文提出TransDex，一种基于点云重建预训练的3D视觉-触觉融合运动策略。具体而言，我们首先提出一种基于Transformer的自监督点云重建预训练方法，该方法能从交互点云中准确恢复物体的3D结构，即使在添加随机噪声和大规模遮挡时也能保持准确性。在此基础上，TransDex采用细粒度分层方案的感知编码，并通过多轮注意力机制自适应融合机械臂和精细手的特征，以实现差异化运动预测。在真实机器人系统上进行的透明物体操作实验结果表明，TransDex优于现有基线方法。进一步分析验证了TransDex的泛化能力和其各个组件的有效性。
英文摘要：
Dexterous manipulation enables complex tasks but suffers from self-occlusion, severe depth noise, and depth information loss when manipulating transparent objects. To solve this problem, this paper proposes TransDex, a 3D visuo-tactile fusion motor policy based on point cloud reconstruction pre-training. Specifically, we first propose a self-supervised point cloud reconstruction pre-training approach based on Transformer. This method accurately recovers the 3D structure of objects from interactive point clouds of dexterous hands, even when random noise and large-scale masking are added. Building on this, TransDex is constructed in which perceptual encoding adopts a fine-grained hierarchical scheme and multi-round attention mechanisms adaptively fuse features of the robotic arm and dexterous hand to enable differentiated motion prediction. Results from transparent object manipulation experiments conducted on a real robotic system demonstrate that TransDex outperforms existing baseline methods. Further analysis validates the generalization capabilities of TransDex and the effectiveness of its individual components.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
