ReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation

General-purpose multimodal foundation models are increasingly capable of directly controlling robots, yet improving their manipulation capabilities through autonomous interaction remains challenging. A key challenge to such self-improvement is learning from failures in complex manipulation tasks involving cross-stage dependencies, where an action may succeed locally but cause a later stage to fail.

From the abstract. Our summary is in progress.