# Failure Forecasting Boosts Robustness of Sim2Real Rhythmic Insertion Policies

arXiv: https://arxiv.org/abs/2507.06519 · HTML: https://mlsanity.com/paper/2507.06519/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 31 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 26 of 26)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [FoundationPose: Unified 6D Pose Estimation and Tracking of Novel Objects](https://mlsanity.com/paper/2312.08344/) (2023)
- [Safe Learning in Robotics: From Learning-Based Control to Safe Reinforcement Learning](https://mlsanity.com/paper/2108.06266/) (2021)
- [Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners](https://mlsanity.com/paper/2307.01928/) (2023)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [Recovery RL: Safe Reinforcement Learning with Learned Recovery Zones](https://mlsanity.com/paper/2010.15920/) (2020)
- [Learning to be Safe: Deep RL with a Safety Critic](https://mlsanity.com/paper/2010.14603/) (2020)
- [Robot Learning on the Job: Human-in-the-Loop Autonomy and Learning During Deployment](https://mlsanity.com/paper/2211.08416/) (2022)
- [Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks](https://mlsanity.com/paper/1907.13098/) (2019)
- [Safe Reinforcement Learning with Model Uncertainty Estimates](https://mlsanity.com/paper/1810.08700/) (2018)
- [Factory: Fast Contact for Robotic Assembly](https://mlsanity.com/paper/2205.03532/) (2022)
- [IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality](https://mlsanity.com/paper/2305.17110/) (2023)
- [Sample-Efficient Safety Assurances using Conformal Prediction](https://mlsanity.com/paper/2109.14082/) (2021)
- [Model-Based Runtime Monitoring with Interactive Imitation Learning](https://mlsanity.com/paper/2310.17552/) (2023)
- [Interactive Robot Learning from Verbal Correction](https://mlsanity.com/paper/2310.17555/) (2023)
- [Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2005.10872/) (2020)
- [Failure Prediction with Statistical Guarantees for Vision-Based Robot Control](https://mlsanity.com/paper/2202.05894/) (2022)
- [Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks](https://mlsanity.com/paper/2110.12618/) (2021)
- [Back to the Manifold: Recovering from Out-of-Distribution States](https://mlsanity.com/paper/2207.08673/) (2022)
- [InsertionNet -- A Scalable Solution for Insertion](https://mlsanity.com/paper/2104.14223/) (2021)
- [RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning](https://mlsanity.com/paper/2104.00543/) (2021)
- [Learning on the Job: Self-Rewarding Offline-to-Online Finetuning for Industrial Insertion of Novel Connectors from Vision](https://mlsanity.com/paper/2210.15206/) (2022)
- [RecoveryChaining: Learning Local Recovery Policies for Robust Manipulation](https://mlsanity.com/paper/2410.13979/) (2024)
- [Integrating Model-based Control and RL for Sim2Real Transfer of Tight Insertion Policies](https://mlsanity.com/paper/2505.11858/) (2025)
- [Accelerating Robot Learning of Contact-Rich Manipulations: A Curriculum Learning Study](https://mlsanity.com/paper/2204.12844/) (2022)

## Built on by (most cited first, 2 of 2)

- [Foresight Residual RL for Long-Horizon Robot Manipulation with Vision-Language-Action Models](https://mlsanity.com/paper/2607.16506/) (2026)
- [Beyond Policy Alignment: Closing the Planning-Learning Loop for Robot Control with Learned World Models](https://mlsanity.com/paper/2609.39751/) (2026)

## Abstract (arXiv)

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.