32 citations · 177 across the 32 of their papers we have counts for
4 papers · 2 filters
Learn to Grasp with Less Supervision: A Data-Efficient Maximum Likelihood Grasp Sampling Loss
Xinghao Zhu, Yefan Zhou, Yongxiang Fan +3
Robotic grasping for a diverse set of objects is essential in many robot manipulation tasks. One promising approach is to learn deep grasping models from large training datasets of…
Constrained Iterative LQG for Real-Time Chance-Constrained Gaussian Belief Space Planning
Jianyu Chen, Yutaka Shimizu, Liting Sun +2
Motion planning under uncertainty is of significant importance for safety-critical systems such as autonomous vehicles. Such systems have to satisfy necessary constraints (e.g., co…
Model-based Constrained Reinforcement Learning using Generalized Control Barrier Function
Haitong Ma, Jianyu Chen, Shengbo Eben Li +4
Model information can be used to predict future trajectories, so it has huge potential to avoid dangerous region when implementing reinforcement learning (RL) on real-world tasks,…
A Safe Hierarchical Planning Framework for Complex Driving Scenarios based on Reinforcement Learning
Jinning Li, Liting Sun, Jianyu Chen +2
Autonomous vehicles need to handle various traffic conditions and make safe and efficient decisions and maneuvers. However, on the one hand, a single optimization/sampling-based mo…