外观
论文arXiv 2610.0353826 天前
RATE：用于富接触机器人操作的风险感知触觉编码
原文链接：RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation（arXiv 2610.03538 · 发布日期：2026-10-02）
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
RATE针对富接触操作中的触觉歧义，学习任务条件风险表征，并在仿真和真实世界实验中验证成功率提升。
核心要点
- 相似触觉观测可能来自噪声、必要变化或不良接触，使下游策略难以区分风险。
- RATE通过历史条件预测获取交互上下文，并用告警监督关联任务条件风险。
- 风险感知表征经轻量残差适配器补充常规触觉特征，用于下游策略。
- 仿真与真实世界实验显示任务成功率显著提升，消融验证两类模块互补。
验证范围与局限：验证限于摘要所述仿真和真实世界实验，具体任务与机器人平台未说明。
英文摘要 (Abstract)
Tactile sensing is particularly valuable for contact-rich robotic manipulation. Recent work has made substantial progress in tactile representation learning for robotic manipulation. However, similar tactile observations can arise from interaction conditions with very different task-risk implications, such as sensor noise, task-necessary variations, and emerging undesirable contact. Without context-grounded risk information, these cases can be ambiguous to downstream policies, leading to unnecessary corrections to benign variations or delayed responses to genuinely risky contact. To address this limitation, we propose Risk-Aware Tactile Encoding (RATE), which learns tactile representations that encode task-conditioned interaction risk. Specifically, history-conditioned prediction captures interaction context, while alert supervision associates this context with task-conditioned risk. The learned risk-aware representation complements conventional tactile features through a lightweight residual adapter. Experiments in both simulation and the real world demonstrate substantial improvements in task success, with controlled ablations confirming the complementary benefits of alert-guided learning and predictive temporal modeling.
作者：Yuyao Jiang, Haichao Liu, Jiarui Zheng, Zihan Ding, Weihao Yuan, Ziwei Wang
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