286 citations · 542 across the 33 of their papers we have counts for
34 papers
Learn from Incomplete Tactile Data: Tactile Representation Learning with Masked Autoencoders
Guanqun Cao, Jiaqi Jiang, Danushka Bollegala +1
The missing signal caused by the objects being occluded or an unstable sensor is a common challenge during data collection. Such missing signals will adversely affect the results o…
Multimodal Zero-Shot Learning for Tactile Texture Recognition
Guanqun Cao, Jiaqi Jiang, Danushka Bollegala +2
Tactile sensing plays an irreplaceable role in robotic material recognition. It enables robots to distinguish material properties such as their local geometry and textures, especia…
Reinforcement Learning in Robotic Motion Planning by Combined Experience-based Planning and Self-Imitation Learning
Sha Luo, Lambert Schomaker
High-quality and representative data is essential for both Imitation Learning (IL)- and Reinforcement Learning (RL)-based motion planning tasks. For real robots, it is challenging…
Beyond Flat GelSight Sensors: Simulation of Optical Tactile Sensors of Complex Morphologies for Sim2Real Learning
Daniel Fernandes Gomes, Paolo Paoletti, Shan Luo
Recently, several morphologies, each with its advantages, have been proposed for the \textit{GelSight} high-resolution tactile sensors. However, existing simulation methods are lim…
Robotic Perception of Transparent Objects: A Review
Jiaqi Jiang, Guanqun Cao, Jiankang Deng +2
Transparent object perception is a rapidly developing research problem in artificial intelligence. The ability to perceive transparent objects enables robots to achieve higher leve…
Tacchi: A Pluggable and Low Computational Cost Elastomer Deformation Simulator for Optical Tactile Sensors
Zixi Chen, Shixin Zhang, Shan Luo +2
Simulation is widely applied in robotics research to save time and resources. There have been several works to simulate optical tactile sensors that leverage either a smoothing met…