ReTrace: Cross-Stage Failure Attribution for Self-Improving
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: ReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation. This paper presents ReTrace, a self-improvement framework for robotic manipulation that enables autonomous physical interaction and retrospective reasoning without provided demonstrations or model-parameter updates. The …
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“arXiv:2610.15814v1 Announce Type: new Abstract: 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”
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- arXiv Robotics — research abstractsReTrace: Cross-Stage Failure Attribution for Self-Improving Robotic Manipulation
This paper presents ReTrace, a self-improvement framework for robotic manipulation that enables autonomous physical interaction and retrospective reasoning without provided demonstrations or model-parameter updates. The method traces downstream failures to earlier decisions, proposes targeted corrections, and evaluates them through subsequent interactions. Evaluated on four real-world tasks using a Franka Research 3 robot, ReTrace achieves significant improvements in success rates for plug insertion and ball pouring tasks.
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