9 citations · 14 across the 8 of their papers we have counts for
8 papers
Improving Compositional Generation with Diffusion Models Using Lift Scores
Chenning Yu, Sicun Gao
We introduce a novel resampling criterion using lift scores, for improving compositional generation in diffusion models. By leveraging the lift scores, we evaluate whether generate…
Controllable Motion Generation via Diffusion Modal Coupling
Luobin Wang, Hongzhan Yu, Chenning Yu +2
Diffusion models have recently gained significant attention in robotics due to their ability to generate multi-modal distributions of system states and behaviors. However, a key ch…
Efficient Motion Planning for Manipulators with Control Barrier Function-Induced Neural Controller
Mingxin Yu, Chenning Yu, M-Mahdi Naddaf-Sh +3
Sampling-based motion planning methods for manipulators in crowded environments often suffer from expensive collision checking and high sampling complexity, which make them difficu…
Iterative Reachability Estimation for Safe Reinforcement Learning
Milan Ganai, Zheng Gong, Chenning Yu +2
Ensuring safety is important for the practical deployment of reinforcement learning (RL). Various challenges must be addressed, such as handling stochasticity in the environments,…
Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance
Hongzhan Yu, Chiaki Hirayama, Chenning Yu +2
There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and th…
Accelerating Multi-Agent Planning Using Graph Transformers with Bounded Suboptimality
Chenning Yu, Qingbiao Li, Sicun Gao +1
Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to…
Learning Control Admissibility Models with Graph Neural Networks for Multi-Agent Navigation
Chenning Yu, Hongzhan Yu, Sicun Gao
Deep reinforcement learning in continuous domains focuses on learning control policies that map states to distributions over actions that ideally concentrate on the optimal choices…
Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding
Ruipeng Zhang, Chenning Yu, Jingkai Chen +2
Learning-based methods have shown promising performance for accelerating motion planning, but mostly in the setting of static environments. For the more challenging problem of plan…