Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning
- 1 November 2026
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Robotics and Automation Letters
- Vol. 11 (11) , 13327-13334
Abstract
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This publication has 20 references indexed in Scilit:
- Scaling Verification Can Be More Effective than Scaling Policy Learning for Vision-Language-Action Alignment2026
- Uncertainty Comes for Free: Human-in-the-Loop Policies with Diffusion Models2026
- MoE-DP: An MoE-Enhanced Diffusion Policy for Robust Long-Horizon Robotic Manipulation with Skill Decomposition and Failure Recovery2026
- Safe Policies Post-Training: Constraining Streaming Flow Models for Adapting Learned Robot Trajectory DistributionsIEEE Robotics and Automation Letters, 2026
- DOSE3: Diffusion-Based Unified Out-of-Distribution Detection on $\mathbb{SE}(3)$ TrajectoriesIEEE Robotics and Automation Letters, 2025
- Structurally-Fused Goal-Aware Network Distillation for Anomaly Detection in Diffusion Policy2025
- Reliable Robotic Task Execution in the Face of AnomaliesIEEE Robotics and Automation Letters, 2025
- Teaching Periodic Stable Robot Motion Generation via SketchIEEE Robotics and Automation Letters, 2024
- Global and Reactive Motion Generation with Geometric Fabric Command Sequences2023
- Mean shift: a robust approach toward feature space analysisIEEE Transactions on Pattern Analysis and Machine Intelligence, 2002