286 citations · 542 across the 33 of their papers we have counts for
6 papers · 1 filter
Surface Following using Deep Reinforcement Learning and a GelSightTactile Sensor
Chen Lu, Jing Wang, Shan Luo
Tactile sensors can provide detailed contact in-formation that can facilitate robots to perform dexterous, in-hand manipulation tasks. One of the primitive but important tasks is s…
Fly Safe: Aerial Swarm Robotics using Force Field Particle Swarm Optimisation
Lauren Parker, James Butterworth, Shan Luo
Particle Swarm Optimisation (PSO) is a powerful optimisation algorithm that can be used to locate global maxima in a search space. Recent interest in swarms of Micro Aerial Vehicle…
GarmNet: Improving Global with Local Perception for Robotic Laundry Folding
Daniel Fernandes Gomes, Shan Luo, Luis F. Teixeira
Developing autonomous assistants to help with domestic tasks is a vital topic in robotics research. Among these tasks, garment folding is one of them that is still far from being a…
Neural Logic Reinforcement Learning
Zhengyao Jiang, Shan Luo
Deep reinforcement learning (DRL) has achieved significant breakthroughs in various tasks. However, most DRL algorithms suffer a problem of generalizing the learned policy which ma…
Fully Convolutional One-Shot Object Segmentation for Industrial Robotics
Benjamin Schnieders, Shan Luo, Gregory Palmer +1
The ability to identify and localize new objects robustly and effectively is vital for robotic grasping and manipulation in warehouses or smart factories. Deep convolutional neural…
"Touching to See" and "Seeing to Feel": Robotic Cross-modal SensoryData Generation for Visual-Tactile Perception
Jet-Tsyn Lee, Danushka Bollegala, Shan Luo
The integration of visual-tactile stimulus is common while humans performing daily tasks. In contrast, using unimodal visual or tactile perception limits the perceivable dimensiona…