activity
20192021
most citedOff-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)

10 citations · 17 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2021

GSG: A Granary Soft Gripper with Mechanical Force Sensing via 3-Dimensional Snap-Through Structure

Huixu Dong, Chao-Yu Chen, Chen Qiu +2

Grasping is an essential capability for most robots in practical applications. Soft robotic grippers are considered as a critical part of robotic grasping and have attracted consid…

cs.RO20211 cited

Real-time Human-Robot Collaborative Manipulations of Cylindrical and Cubic Objects via Geometric Primitives and Depth Information

Huixu Dong, Jiadong Zhou, Haoyong Yu

Many objects commonly found in household and industrial environments are represented by cylindrical and cubic shapes. Thus, it is available for robots to manipulate them through th…

cs.LG202010 cited

Off-policy Maximum Entropy Reinforcement Learning : Soft Actor-Critic with Advantage Weighted Mixture Policy(SAC-AWMP)

Zhimin Hou, Kuangen Zhang, Yi Wan +3

The optimal policy of a reinforcement learning problem is often discontinuous and non-smooth. I.e., for two states with similar representations, their optimal policies can be signi…

cs.RO20195 cited

Teach Biped Robots to Walk via Gait Principles and Reinforcement Learning with Adversarial Critics

Kuangen Zhang, Zhimin Hou, Clarence W. de Silva +2

Controlling a biped robot to walk stably is a challenging task considering its nonlinearity and hybrid dynamics. Reinforcement learning can address these issues by directly mapping…

cs.RO20191 cited

A Sliding Mode Force and Position Controller Synthesis for Series Elastic Actuators

Emre Sariyildiz, Rahim Mutlu, Haoyong Yu

This paper deals with the robust force and position control problems of Series Elastic Actuators. It is shown that a Series Elastic Actuator's force control problem can be describe…