4D-WAM: Training world action models with 3D trajectory supervision, zero inference overhead
量子位 · wechat · 2026-08-16
4D-WAM introduces a model-agnostic training strategy using 3D trajectory fields to teach world action models (WAMs) dynamic 3D understanding via MotionAlignment and DestinationAlignment. Tested on FastWAM-Joint and Lingbot-VA, it improves performance on standard benchmarks and out-of-distribution perturbations, and boosts real robot task success. Training overhead 2%, inference overhead zero.
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