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Grasping performance can be dramatically improved with a single view adjustment that leverages affordance fields, bypassing the need for complex scene reconstruction.
VLAConf offers a computationally cheap, architecture-agnostic way to estimate task success confidence for robot manipulation by turning frozen VLA representations into anomaly scores.
