Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies

• URL: http://arxiv.org/abs/2507.06519v1
• Date: Wed, 09 Jul 2025 03:38:44 GMT
• Title: Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies
• Abstract summary: This paper addresses the challenges of Rhythmic Insertion Tasks (RIT), where a robot must repeatedly perform high-precision insertions.<n>We propose a framework that integrates a reinforcement learning-based insertion policy with a failure forecasting module.<n>Our method achieves a high one-time success rate and robustly maintains performance over long-horizon repetitive tasks.
• Score: 32.99644520625179
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper addresses the challenges of Rhythmic Insertion Tasks (RIT), where a robot must repeatedly perform high-precision insertions, such as screwing a nut into a bolt with a wrench. The inherent difficulty of RIT lies in achieving millimeter-level accuracy and maintaining consistent performance over multiple repetitions, particularly when factors like nut rotation and friction introduce additional complexity. We propose a sim-to-real framework that integrates a reinforcement learning-based insertion policy with a failure forecasting module. By representing the wrench's pose in the nut's coordinate frame rather than the robot's frame, our approach significantly enhances sim-to-real transferability. The insertion policy, trained in simulation, leverages real-time 6D pose tracking to execute precise alignment, insertion, and rotation maneuvers. Simultaneously, a neural network predicts potential execution failures, triggering a simple recovery mechanism that lifts the wrench and retries the insertion. Extensive experiments in both simulated and real-world environments demonstrate that our method not only achieves a high one-time success rate but also robustly maintains performance over long-horizon repetitive tasks.

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