Papers

SplineWAM: Adaptive Action Horizons for World Action Models via B-Spline Representations

This preprint introduces SplineWAM, which replaces the fixed-length action chunk of a world action model with a fixed-size window of cubic B-spline knot times and control points, letting each prediction adapt its temporal horizon and resolution. On LIBERO-Plus and RoboCasa it improves success rate by 8.2 and 4.4 points over an action-chunking WAM while issuing 22% and 26% fewer policy calls per episode; on three real bimanual tasks it matches or beats the baseline while decoding 1.2–1.6× more executed motion per call. This matters for cloud-served robot fleets because fewer calls per task directly increase throughput.