Where Success Breaks: Failure-Boundary Learning for Robust V
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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: Where Success Breaks: Failure-Boundary Learning for Robust Vision-Language-Action Models. This research addresses the structural asymmetry in Vision-Language-Action (VLA) models trained via supervised fine-tuning (SFT), where expert demonstrations only teach success behavior but not failure boundaries. The pa…
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- arXiv Robotics — research abstractsWhere Success Breaks: Failure-Boundary Learning for Robust Vision-Language-Action Models
This research addresses the structural asymmetry in Vision-Language-Action (VLA) models trained via supervised fine-tuning (SFT), where expert demonstrations only teach success behavior but not failure boundaries. The paper proposes DLS, a method for Failure-Boundary Learning that discovers, localizes, and shapes the boundary between recoverable deviations and task failure using real-grounded behavioral priors and simulated co-training. DLS improves robustness over SFT and online RL baselines, especially in randomized initial states and unseen visual conditions.
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