Author

Gang He

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Preprint Oct 2026

Revealing and Bridging the Physical Executability Gap in Vision-Language Navigation

Continuous vision-and-language navigation (VLN) is typically evaluated by goal-reaching success, assuming it reflects an agent's ability to execute language-grounded behavior. We show that this assumption can fail: permissive contact models in current simulators may mask physically infeasible actions, creating a physic...

Gang He, Zhi-Le Cheng, Ke-Peng Xu et al. · 0 citations

Conference Open access Sep 2026

Beyond Pipeline Mimicry: Expert-level Aesthetic ISP via Reward-Guided Flow

This work proposes AesISP, the first expert-level aesthetic ISP framework formulated as a reward flow model, and establishes a Privileged Prior Distillation paradigm, and introduces a multi-dimensional reward-driven reinforcement learning approach.

Tong Qiao, Kepeng Xu, Gang He et al. · Proceedings of the Thirty-Fi... · 0 citations