Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks. - Chenning Yu; Sicun Gao (NeurIPS, 2021)

Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation. - Chenning Yu; Hongzhan Yu; Sicun Gao (CoRL, 2022)

Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding. - Ruipeng Zhang; Chenning Yu; Jingkai Chen; Chuchu Fan; Sicun Gao (NeurIPS, 2022)

Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality. - Chenning Yu; Qingbiao Li; Sicun Gao; Amanda Prorok (ICRA, 2023)

Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance. - Hongzhan Yu; Chiaki Hirayama; Chenning Yu; Sylvia L. Herbert; Sicun Gao (IROS, 2023)

Iterative Reachability Estimation for Safe Reinforcement Learning. - Milan Ganai; Zheng Gong; Chenning Yu; Sylvia L. Herbert; Sicun Gao (NeurIPS, 2023)

Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller. - Mingxin Yu; Chenning Yu; M.-Mahdi Naddaf-Sh; Devesh Upadhyay; Sicun Gao; Chuchu Fan (ICRA, 2024)

Controllable Motion Generation via Diffusion Modal Coupling. - Luobin Wang; Hongzhan Yu; Chenning Yu; Sicun Gao; Henrik I. Christensen (CoRR, 2025)

Improving Compositional Generation with Diffusion Models Using Lift Scores. - Chenning Yu; Sicun Gao (CoRR, 2025)