12 citations · 17 across the 6 of their papers we have counts for
6 papers
Sim-to-Real Deep Reinforcement Learning with Manipulators for Pick-and-place
Wenxing Liu, Hanlin Niu, Robert Skilton +1
When transferring a Deep Reinforcement Learning model from simulation to the real world, the performance could be unsatisfactory since the simulation cannot imitate the real world…
Sim-and-Real Reinforcement Learning for Manipulation: A Consensus-based Approach
Wenxing Liu, Hanlin Niu, Wei Pan +2
Sim-and-real training is a promising alternative to sim-to-real training for robot manipulations. However, the current sim-and-real training is neither efficient, i.e., slow conver…
Accelerated Sim-to-Real Deep Reinforcement Learning: Learning Collision Avoidance from Human Player
Hanlin Niu, Ze Ji, Farshad Arvin +3
This paper presents a sensor-level mapless collision avoidance algorithm for use in mobile robots that map raw sensor data to linear and angular velocities and navigate in an unkno…
3D Vision-guided Pick-and-Place Using Kuka LBR iiwa Robot
Hanlin Niu, Ze Ji, Zihang Zhu +2
This paper presents the development of a control system for vision-guided pick-and-place tasks using a robot arm equipped with a 3D camera. The main steps include camera intrinsic…
Design, Integration and Sea Trials of 3D Printed Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Cooperative Missions
Hanlin Niu, Ze Ji, Pietro Liguori +2
In recent years, Unmanned Surface Vehicles (USV) have been extensively deployed for maritime applications. However, USV has a limited detection range with sensor installed at the s…
Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle
Hanlin Niu, Ze Ji, Al Savvaris +2
This work aims to develop a model checking method to verify the decision making system of Unmanned Surface Vehicle (USV) in a long range surveillance mission. The scenario in this…