Dynamic Maclaurin-Series-Based Vision-Language-Action Model

Abstract

Motivated by the Maclaurin-series theorem, a dynamic Maclaurin-series-based Vision-Language-Action (DMS-VLA) model introduces a unified approximation framework that replaces per-layer transformer weight tensors with a small set of shared Maclaurin-series templates and layer-specific coefficients, dramatically reducing memory footprint without degrading task performance. By dynamically adjusting the number of series terms per input, using target-action differencing, DMS-VLA adapts computational effort to visual and language complexity. Evaluations on the LIBERO and SIMPLER benchmarks demonstrate that DMS-VLA matches or slightly exceeds state-of-the-art VLA baselines in success rates, while real-robot trials on ALOHA and Emergen platforms confirm comparable real-world efficacy. Deployment on NVIDIA H100, RTX4090 GPUs and Jetson AGX Orin edge hardware yields up to 25%-$55\%\times$ inference speedups, validating DMS-VLA as an efficient, high-quality solution for resource-constrained embodied agents.