14 citations · 20 across the 8 of their papers we have counts for
5 papers · 1 filter
DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch
Shuo Sun, Zekai Gu, Tianchen Sun +5
Realistic and diverse traffic scenarios in large quantities are crucial for the development and validation of autonomous driving systems. However, owing to numerous difficulties in…
CARLA-Loc: Synthetic SLAM Dataset with Full-stack Sensor Setup in Challenging Weather and Dynamic Environments
Yuhang Han, Zhengtao Liu, Shuo Sun +4
The robustness of SLAM (Simultaneous Localization and Mapping) algorithms under challenging environmental conditions is critical for the success of autonomous driving. However, the…
Learning Complicated Manipulation Skills via Deterministic Policy with Limited Demonstrations
Liu Haofeng, Chen Yiwen, Tan Jiayi +1
Combined with demonstrations, deep reinforcement learning can efficiently develop policies for manipulators. However, it takes time to collect sufficient high-quality demonstration…
Real2Sim or Sim2Real: Robotics Visual Insertion using Deep Reinforcement Learning and Real2Sim Policy Adaptation
Yiwen Chen, Xue Li, Sheng Guo +2
Reinforcement learning has shown a wide usage in robotics tasks, such as insertion and grasping. However, without a practical sim2real strategy, the policy trained in simulation co…
Economical Precise Manipulation and Auto Eye-Hand Coordination with Binocular Visual Reinforcement Learning
Yiwen Chen, Sheng Guo, Zedong Zhang +3
Precision robotic manipulation tasks (insertion, screwing, precisely pick, precisely place) are required in many scenarios. Previous methods achieved good performance on such manip…