PROBEACT is a plug-and-play intervention framework that combines hidden-state probing, kinematic failure detection, and CBF-based correction to boost success rates of pre-trained VLA models on the LIBERO-plus benchmark from 69.6% to 74.1%.
Cyclevla: Proactive self-correcting vision-language-action models via subtask backtracking and minimum bayes risk decoding,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.RO 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
Progress-enhanced VLA model raises simulated bimanual furniture assembly success from 48% to 80% across three furniture types and shows 16% drop on real Kinova robot.
citing papers explorer
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ProbeAct: Probe-Guided Training-Free Failure Recovery in Vision-Language-Action Models
PROBEACT is a plug-and-play intervention framework that combines hidden-state probing, kinematic failure detection, and CBF-based correction to boost success rates of pre-trained VLA models on the LIBERO-plus benchmark from 69.6% to 74.1%.
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FurnitureVLA: Learning Long-Horizon Bimanual Furniture Assembly with Vision-Language-Action Model
Progress-enhanced VLA model raises simulated bimanual furniture assembly success from 48% to 80% across three furniture types and shows 16% drop on real Kinova robot.