F4R: Failure-Driven Recognition, Reconstruction, Refinement, and Redeployment for Continual Robot Self-Improvement
Failure for Rising is proposed, a failure-driven real-to-sim-to-real closed-loop learning framework that converts real-world failures into targeted policy improvement and reconstructs each failure as an interactive, object-centric table-top environment that preserves the task-relevant spatial and physical conditions.
Zhuo-Yuan Yu, Jia-Cheng Wang, Tian-Le Liu et al.
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