most citedAdversary A3C for Robust Reinforcement Learning

20 citations · 30 across the 5 of their papers we have counts for

collaborators

5 papers

eess.SY2021★ 4 cited

Integrated Task and Motion Planning for Safe Legged Navigation in Partially Observable Environments

Abdulaziz Shamsah, Zhaoyuan Gu, Jonas Warnke +2

This study proposes a hierarchically integrated framework for safe task and motion planning (TAMP) of bipedal locomotion in a partially observable environment with dynamic obstacle…

cs.RO2021

Reactive Locomotion Decision-Making and Robust Motion Planning for Real-Time Perturbation Recovery

Zhaoyuan Gu, Nathan Boyd, Ye Zhao

In this paper, we examine the problem of push recovery for bipedal robot locomotion and present a reactive decision-making and robust planning framework for locomotion resilient to…

cs.LG2019★ 20 cited

Adversary A3C for Robust Reinforcement Learning

Zhaoyuan Gu, Zhenzhong Jia, Howie Choset

Asynchronous Advantage Actor Critic (A3C) is an effective Reinforcement Learning (RL) algorithm for a wide range of tasks, such as Atari games and robot control. The agent learns p…

eess.SY2019★ 3 cited

State Estimation for Legged Robots Using Contact-Centric Leg Odometry

Shuo Yang, Hans Kumar, Zhaoyuan Gu +3

Our goal is to send legged robots into challenging, unstructured terrains that wheeled systems cannot traverse. Moreover, precise estimation of the robot's position and orientation…

eess.SY2019★ 3 cited

Design and Implementation of a Three-Link Brachiation Robot with Optimal Control Based Trajectory Tracking Controller

Shuo Yang, Zhaoyuan Gu, Ruohai Ge +3

This paper reports the design and implementation of a three-link brachiation robot. The robot is able to travel along horizontal monkey bars using continuous arm swings. We build a…