Gwanghyeon Ji — accepted AI-conference papers

2022

Concurrent Training of a Control Policy and a State Estimator for Dynamic and Robust Legged Locomotion

Gwanghyeon Ji, Juhyeok Mun, Hyeongjun Kim, Jemin Hwangbo

RA-L 2022

In this letter, we propose a locomotion training framework where a control policy and a state estimator are trained concurrently. The framework consists of a policy network which outputs the desired joint positions and a state estimation network which outputs estimates of the robot’s states such as

Cited by 232 Source ↗code ↗Scholar ↗

2021

Legged Robot State Estimation With Dynamic Contact Event Information

Joon-Ha Kim, Seungwoo Hong, Gwanghyeon Ji, Seunghun Jeon, Jemin Hwangbo, Jun-Ho Oh +1

RA-L 2021

This letter presents a state estimation algorithm for the legged robot by defining the problem as a Maximum A Posteriori (MAP) estimation problem and solving the problem with the Gauss-Newton algorithm. Moreover, marginalization by the Schur Complement method is adopted to make a fixed size problem.

Cited by 49 Source ↗Scholar ↗