AI RESEARCH
Learning to See and Act: Task-Aware Virtual View Exploration for Robotic Manipulation
arXiv CS.CV
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ArXi:2508.05186v5 Announce Type: replace-cross Recent vision-language-action (VLA) models for multi-task robot manipulation often rely on fixed camera setups and shared visual encoders, which limit their performance under occlusions and during cross-task transfer. To address these challenges, we propose Task-aware Virtual View Exploration (TVVE), a framework that learns to select task-relevant virtual camera viewpoints and dynamically re-render observations from a reconstructed scene representation using the selected viewpoints.