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Can a geometry-aware correction layer improve robot manipulation without retraining the VLA policy? This episode explains PARMA's proposed execution layer and the evidence needed to assess its pose-awareness claim. We cover three questions: how gripper-local proxy points connect rotation to collision risk, why joint safe-success metrics matter, and how to distinguish geometric improvements from changes in intervention strength. The discussion places the proposal alongside classical quadratic-program safety filters, AEGIS, and recent route-guided action repair. The experimental domain spans SafeLIBERO Spatial manipulation in simulation and a two-scene SO101 hardware feasibility study. For practitioners, the episode focuses on the interface between a learned action proposal and physical execution: what gets represented, what gets corrected, and what counts as a productive outcome. The central unresolved issue is attribution—whether pose-aware geometry or the accompanying correction rules drive the gains. This is an abstract-based analysis: the accessible record supplied the mechanism outline and headline results, but the IEEE full text was unavailable for checking baseline settings and ablations. What would convince you that better gripper geometry, rather than gentler intervention, is doing the work?
