Search Results for author: Zhaoming Xie

Found 7 papers, 4 papers with code

GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model

no code implementations20 Apr 2021 Zhaoming Xie, Xingye Da, Buck Babich, Animesh Garg, Michiel Van de Panne

Model-free reinforcement learning (RL) for legged locomotion commonly relies on a physics simulator that can accurately predict the behaviors of every degree of freedom of the robot.

Learning a Contact-Adaptive Controller for Robust, Efficient Legged Locomotion

no code implementations21 Sep 2020 Xingye Da, Zhaoming Xie, David Hoeller, Byron Boots, Animashree Anandkumar, Yuke Zhu, Buck Babich, Animesh Garg

We present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped (the Unitree Laikago).


ALLSTEPS: Curriculum-driven Learning of Stepping Stone Skills

1 code implementation9 May 2020 Zhaoming Xie, Hung Yu Ling, Nam Hee Kim, Michiel Van de Panne

Humans are highly adept at walking in environments with foot placement constraints, including stepping-stone scenarios where the footstep locations are fully constrained.


Learning to Correspond Dynamical Systems

no code implementations L4DC 2020 Nam Hee Kim, Zhaoming Xie, Michiel Van de Panne

Many dynamical systems exhibit similar structure, as often captured by hand-designed simplified models that can be used for analysis and control.

Iterative Reinforcement Learning Based Design of Dynamic Locomotion Skills for Cassie

1 code implementation22 Mar 2019 Zhaoming Xie, Patrick Clary, Jeremy Dao, Pedro Morais, Jonathan Hurst, Michiel Van de Panne

Deep reinforcement learning (DRL) is a promising approach for developing legged locomotion skills.


Feedback Control For Cassie With Deep Reinforcement Learning

3 code implementations15 Mar 2018 Zhaoming Xie, Glen Berseth, Patrick Clary, Jonathan Hurst, Michiel Van de Panne

By formulating a feedback control problem as finding the optimal policy for a Markov Decision Process, we are able to learn robust walking controllers that imitate a reference motion with DRL.


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