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About this paper
Zhilin He, Guoquan Qi, Changliu Liu
MGA uses learned policy proposals and model-based rollouts to refine robot-arm motion-and-impedance sequences for contact-rich tasks. It shapes updates around task residuals, then applies local residual correction before executing and replanning. Across 13 surface-scanning conditions, MGA achieves 96.03% mean contact-valid coverage; without correction, coverage falls to 84.75% despite 99.88% terminal path progress. This correction is local, not a hard-safety guarantee.
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