Explicit geometry-based feasibility supervision added to diffusion VLA training leads to better physical reliability, task success, and faster learning with limited data in manipulation tasks.
Regularized deep signed distance fields for reactive motion generation
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Can Explicit Physical Feasibility Benefit VLA Learning? An Empirical Study
Explicit geometry-based feasibility supervision added to diffusion VLA training leads to better physical reliability, task success, and faster learning with limited data in manipulation tasks.