Author

Ye Shi

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Preprint Aug 2026

Hierarchical Constrained Reinforcement Learning with Dynamic Boundary for Spatio-Temporal Vehicle-to-Grid Scheduling

The rapid proliferation of Electric Vehicles (EVs) introduces significant spatio-temporal uncertainties into power grids, while Vehicle-to-Grid (V2G) technology offers critical flexibility through bidirectional power flow. However, integrating large-scale EVs into the Optimal Power Flow framework presents substantial c...

Haoyu Yan, Shutong Ding, Jiebao Zhang et al. · 0 citations

Jul 2026

A Predict-then-Schedule framework for Power Distribution Networks with AI Data Centers

An end-to-end Predict-Then-Schedule (PTS) framework that integrates upstream workload prediction with downstream scheduling optimization and maps input features directly to optimal scheduling and enables gradient-based training is proposed.

Si-Qi Yan, Jie-Bao Zhang, Xianhong Yao et al. · arXiv.org · 0 citations

Preprint Aug 2026

SelfWAM: A Self-Grounded Unified World Action Model for Fast Robot Control

SelfWAM is introduced, a unified self-grounded WAM built on a modality-specialized Mixture-of-Transformers (MoT) architecture that jointly predicts actions, action-conditioned future RGB frames, and robot self-masks, thereby grounding future prediction in the robot's visible body and its action-induced motion.

Bikang Pan, Fan Liu, Haotao Lu et al. · 5 citations