2 results about "Merit function" patented technology

The application provides a dynamic memory enhancement method and system for a visual-language-action model and a storage medium, and solves the problems of short-sighted memory, memory pollution and inability to dynamically adjust the fusion weight of the existing VLA model in a long-distance task. The application comprises: constructing a working memory and retrieving historical memory from a perception-cognition-reward memory bank; using a gating network with trainable parameters to adaptively weight and fuse the current memory and the historical memory to generate an enhanced working memory; using a progress evaluator to calculate a task progress difference value based on the enhanced working memory to generate a single-step dense reward; using the reward as a merit function to perform online policy optimization on the gating network parameters; and when the memory bank is full, merging adjacent entries based on the weighted indicators of feature similarity and reward similarity; and finally outputting a motion sequence by a motion expert network. The application can be used for robot long-time sequence task learning and adaptive control in a dynamic environment.

A wavefront correction system and method with evaluation function switching and derivative-free trust region (RDR) is disclosed. This invention relates to the field of optimization and control technology for wavefront-free adaptive optics systems, specifically to a wavefront correction system and method with evaluation function switching and derivative-free trust region (RDR) optimization. The invention organically integrates a phased evaluation function strategy with a model-based derivative-free trust region optimization algorithm, achieving seamless switching of the evaluation function through observation data reuse. The method includes the following steps: simultaneously defining a first evaluation function and a second evaluation function; performing first-stage derivative-free trust region wavefront correction based on the evaluation function, while simultaneously constructing an observation data reuse buffer; switching to second-stage derivative-free trust region wavefront correction; selecting historical sampling points from the observation data reuse buffer, performing second-stage derivative-free trust region wavefront correction based on the observation data, and obtaining the optimal sampling point; updating the wavefront correction value based on the optimal sampling point.