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Zijun Xia

Publications and source records attributed to Zijun Xia.

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A new three-operator splitting method for the monotone inclusion problem

This paper studies a class of monotone inclusion problems in a real Hilbert space involving the sum of three operators, where two are maximal monotone and the third is cocoercive. The Davis--Yin three-operator splitting method extends the two-operator splitting methods -- namely the forward-backward method and the Douglas--Rachford method -- to the three-operator setting. In addition, two other common splitting methods for two-operator problems are the reflected forward-backward and forward-reflected-backward methods. While several three-operator extensions exist for each of these methods individually, a unified framework that generalizes both remains absent. This raises the question: can they be extended to the three-operator case within a single algorithm? To address this, we propose a new splitting algorithm that unifies the Douglas--Rachford, reflected forward-backward, and forward-reflected-backward methods as special cases. We prove its weak convergence and establish its sublinear convergence rate for convex optimization problems under appropriate stepsize conditions. Finally, we present numerical experiments to validate the theoretical properties and demonstrate the effectiveness of the proposed method.

math.OC

Astra: Toward General-Purpose Mobile Robots via Hierarchical Multimodal Learning

Modern robot navigation systems encounter difficulties in diverse and complex indoor environments. Traditional approaches rely on multiple modules with small models or rule-based systems and thus lack adaptability to new environments. To address this, we developed Astra, a comprehensive dual-model architecture, Astra-Global and Astra-Local, for mobile robot navigation. Astra-Global, a multimodal LLM, processes vision and language inputs to perform self and goal localization using a hybrid topological-semantic graph as the global map, and outperforms traditional visual place recognition methods. Astra-Local, a multitask network, handles local path planning and odometry estimation. Its 4D spatial-temporal encoder, trained through self-supervised learning, generates robust 4D features for downstream tasks. The planning head utilizes flow matching and a novel masked ESDF loss to minimize collision risks for generating local trajectories, and the odometry head integrates multi-sensor inputs via a transformer encoder to predict the relative pose of the robot. Deployed on real in-house mobile robots, Astra achieves high end-to-end mission success rate across diverse indoor environments.

cs.RO