activity
20182023
most citedReaching, Grasping and Re-grasping: Learning Multimode Grasping Skills

4 citations · 8 across the 4 of their papers we have counts for

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5 papers · 1 filter

cs.RO2023

Modular Neural Network Policies for Learning In-Flight Object Catching with a Robot Hand-Arm System

Wenbin Hu, Fernando Acero, Eleftherios Triantafyllidis +2

We present a modular framework designed to enable a robot hand-arm system to learn how to catch flying objects, a task that requires fast, reactive, and accurately-timed robot moti…

cs.RO2023

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…

cs.RO20204 cited

Reaching, Grasping and Re-grasping: Learning Multimode Grasping Skills

Wenbin Hu, Chuanyu Yang, Kai Yuan +1

The ability to adapt to uncertainties, recover from failures, and coordinate between hand and fingers are essential sensorimotor skills for fully autonomous robotic grasping. In th…

cs.RO20202 cited

Learning Pregrasp Manipulation of Objects from Ungraspable Poses

Zhaole Sun, Kai Yuan, Wenbin Hu +2

In robotic grasping, objects are often occluded in ungraspable configurations such that no pregrasp pose can be found, eg large flat boxes on the table that can only be grasped fro…

cs.RO2018

Comparison Study of Nonlinear Optimization of Step Durations and Foot Placement for Dynamic Walking

Wenbin Hu, Iordanis Chatzinikolaidis, Kai Yuan +1

This paper studies bipedal locomotion as a nonlinear optimization problem based on continuous and discrete dynamics, by simultaneously optimizing the remaining step duration, the n…