1 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.RO2023
Sim-to-Real Model-Based and Model-Free Deep Reinforcement Learning for Tactile Pushing
Max Yang, Yijiong Lin, Alex Church +4
Object pushing presents a key non-prehensile manipulation problem that is illustrative of more complex robotic manipulation tasks. While deep reinforcement learning (RL) methods ha…
cs.RO2023★ 1 cited
Bi-Touch: Bimanual Tactile Manipulation with Sim-to-Real Deep Reinforcement Learning
Yijiong Lin, Alex Church, Max Yang +4
Bimanual manipulation with tactile feedback will be key to human-level robot dexterity. However, this topic is less explored than single-arm settings, partly due to the availabilit…
cs.RO2023★ 1 cited
Tac-VGNN: A Voronoi Graph Neural Network for Pose-Based Tactile Servoing
Wen Fan, Max Yang, Yifan Xing +2
Tactile pose estimation and tactile servoing are fundamental capabilities of robot touch. Reliable and precise pose estimation can be provided by applying deep learning models to h…