用软腕和触觉记忆实现鲁棒的插销操作，无需重新编程即可适应新情况。
Tactile Memory with Soft Robot: Robust Object Insertion via Masked Encoding and Soft Wrist
- 通过掩码预测学习触觉与动作的时空关联，自动提取任务特征。
- 在真实机器人上对多种插销测试，成功率全面超越基线。
- 适合需要安全接触探索和灵活适应的工业装配场景。
触觉记忆是执行高接触密度任务（如不确定条件下的插锁操作）的关键能力。本文提出触觉记忆软机器人系统（TaMeSo-bot），结合软腕与基于触觉检索的控制策略，实现安全且鲁棒的操作。软腕支持数据采集中的安全接触探索，而触觉记忆则通过检索过往示范实现对未见场景的灵活适应。核心为掩码触觉轨迹变换器（MAT$^\text{3}$），联合建模机器人动作、分布式触觉反馈、力矩测量及本体感知信号的时空交互。通过掩码标记预测，MAT$^\text{3}$ 在上下文中推断缺失感官信息，自主提取任务相关特征，无需显式子任务分割。我们在包含多种插销与工况的真实机器人实验中验证该方法。大量评估表明，MAT$^\text{3}$ 在所有条件下均优于基线，并展现出对未见插销与工况的强大适应能力。
原文摘要 · Abstract (English)
Tactile memory, the ability to store and retrieve touch-based experience, is critical for contact-rich tasks such as key insertion under uncertainty. To replicate this capability, we introduce Tactile Memory with Soft Robot (TaMeSo-bot), a system that integrates a soft wrist with tactile retrieval-based control to enable safe and robust manipulation. The soft wrist allows safe contact exploration during data collection, while tactile memory reuses past demonstrations via retrieval for flexible adaptation to unseen scenarios. The core of this system is the Masked Tactile Trajectory Transformer (MAT$^\text{3}$), which jointly models spatiotemporal interactions between robot actions, distributed tactile feedback, force-torque measurements, and proprioceptive signals. Through masked-token prediction, MAT$^\text{3}$ learns rich spatiotemporal representations by inferring missing sensory information from context, autonomously extracting task-relevant features without explicit subtask segmentation. We validate our approach on peg-in-hole tasks with diverse pegs and conditions in real-robot experiments. Our extensive evaluation demonstrates that MAT$^\text{3}$ achieves higher success rates than the baselines over all conditions and shows remarkable capability to adapt to unseen pegs and conditions.
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