Problem
Research questions and friction points this paper is trying to address.
tactile sensing
contact-rich manipulation
risk-aware representation
tactile ambiguity
RATE: Risk-Aware Tactile Encoding for Contact-rich Robotic Manipulation
🤖 AI Summary
This study addresses the challenge in tactile perception where similar observations often correspond to distinct risks, leading to policy misjudgments or delayed responses. To this end, we propose a risk-aware tactile encoding framework that, for the first time, explicitly integrates task-conditioned risk information into tactile representation learning. Specifically, the method captures interaction context through history-conditioned prediction, correlates task-specific risks via an alarm supervision mechanism, and enhances feature expressiveness using lightweight residual adapters. This approach effectively resolves the ambiguity inherent in contact judgment. Extensive experiments demonstrate significant improvements in manipulation success rates across both simulated and real-world environments, validating the complementary advantages of early-warning learning and temporal modeling.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Risk-Aware Tactile Encoding
Contact-rich Manipulation
Alert Supervision
Residual Adapter
Predictive Temporal Modeling
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