3 citations · 3 across the 4 of their papers we have counts for
4 papers
PhyVisGen: Physically and Visually High-Fidelity Robotic Manipulation Data Generation
Yu Zheng, Qiyu Feng, Yixin Wu +6
Large-scale manipulation demonstrations are essential for learning robust visuomotor policies, yet real-world data collection is expensive and difficult to scale. Simulation offers…
Crossing the Reality Gap in Tactile-Based Learning
Ya-Yen Tsai, Bidan Huang, Yu Zheng +3
Tactile sensors are believed to be essential in robotic manipulation, and prior works often rely on experts to reason the sensor feedback and design a controller. With the recent a…
Dexterous In-Hand Manipulation of Slender Cylindrical Objects through Deep Reinforcement Learning with Tactile Sensing
Wenbin Hu, Bidan Huang, Wang Wei Lee +3
Continuous in-hand manipulation is an important physical interaction skill, where tactile sensing provides indispensable contact information to enable dexterous manipulation of sma…
Explainable Hierarchical Imitation Learning for Robotic Drink Pouring
Dandan Zhang, Yu Zheng, Qiang Li +3
To accurately pour drinks into various containers is an essential skill for service robots. However, drink pouring is a dynamic process and difficult to model. Traditional deep imi…