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Can a robot policy learn better three-dimensional control without running a geometric model every time it acts? This episode of *Embodied AI 101* examines how SLA uses spatial supervision during continual pretraining and downstream adaptation. We cover three questions: how OmniVGGT features become training targets for a VLA; why the alignment-on versus alignment-off scaling experiment matters; and what the RoboDojo-Real leaderboard establishes about the resulting system. The discussion spans simulated bimanual manipulation in RoboTwin, perturbation testing in LIBERO-Plus, and physical evaluation across ARX X5, Piper, and Piper X robots. ([arxiv.org](https://arxiv.org/abs/2511.10560)) We connect SLA to its predecessor, Spatial Forcing, explain adaptive layer aggregation and state dropout, and identify the practical entry point through the released training workflow. The central limitation is causal attribution: the real-world leaderboard configuration adds visual history without a memory-matched baseline that isolates spatial alignment. ([arxiv.org](https://arxiv.org/html/2611.08373v1)) For researchers scaling robot foundation models and practitioners adapting pretrained policies, this is a discussion about what supervision buys—not just how much data gets collected. When the target moves, what has your policy learned that lets it move with it?
