Tag
Presents ForeWAM, a direct-policy World Action Model that provides predictive context to action generation via hidden future-slot KV states, avoiding explicit future video decoding while achieving high success rates on LIBERO benchmarks.
Geometric Action Model repurposes a geometric foundation model for robot policy learning, achieving 85.5% on LIBERO-Plus with 6.9 ms inference, 55× faster than baselines.