RATE: 接触の多いロボットマニピュレーションのためのリスク認識型触覚エンコーディング
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
触覚表現にタスク条件付きの接触リスクを組み込み、履歴予測とアラート教師信号で学習する手法を提案し、シミュレーションと実機で成功率を向上させた。
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著者: Yuyao Jiang, Haichao Liu, Jiarui Zheng, Zihan Ding, Weihao Yuan, Ziwei Wang
分類: cs.RO
原文アブストラクト
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.