252 citations · 689 across the 37 of their papers we have counts for
9 papers · 1 filter
Efficient Learning of Voltage Control Strategies via Model-based Deep Reinforcement Learning
Ramij R. Hossain, Tianzhixi Yin, Yan Du +5
This article proposes a model-based deep reinforcement learning (DRL) method to design emergency control strategies for short-term voltage stability problems in power systems. Rece…
A Survey of Deep Learning for Mathematical Reasoning
Pan Lu, Liang Qiu, Wenhao Yu +2
Mathematical reasoning is a fundamental aspect of human intelligence and is applicable in various fields, including science, engineering, finance, and everyday life. The developmen…
Versatile Real-Time Motion Synthesis via Kino-Dynamic MPC with Hybrid-Systems DDP
He Li, Tingnan Zhang, Wenhao Yu +1
Specialized motions such as jumping are often achieved on quadruped robots by solving a trajectory optimization problem once and executing the trajectory using a tracking controlle…
Zero-Shot Retargeting of Learned Quadruped Locomotion Policies Using Hybrid Kinodynamic Model Predictive Control
He Li, Tingnan Zhang, Wenhao Yu +1
Reinforcement Learning (RL) has witnessed great strides for quadruped locomotion, with continued progress in the reliable sim-to-real transfer of policies. However, it remains a ch…
PI-ARS: Accelerating Evolution-Learned Visual-Locomotion with Predictive Information Representations
Kuang-Huei Lee, Ofir Nachum, Tingnan Zhang +3
Evolution Strategy (ES) algorithms have shown promising results in training complex robotic control policies due to their massive parallelism capability, simple implementation, eff…
Enhancing Automated Software Traceability by Transfer Learning from Open-World Data
Jinfeng Lin, Amrit Poudel, Wenhao Yu +3
Software requirements traceability is a critical component of the software engineering process, enabling activities such as requirements validation, compliance verification, and sa…
Learning Semantics-Aware Locomotion Skills from Human Demonstration
Yuxiang Yang, Xiangyun Meng, Wenhao Yu +3
The semantics of the environment, such as the terrain type and property, reveals important information for legged robots to adjust their behaviors. In this work, we present a frame…
Adversarial Motion Priors Make Good Substitutes for Complex Reward Functions
Alejandro Escontrela, Xue Bin Peng, Wenhao Yu +4
Training a high-dimensional simulated agent with an under-specified reward function often leads the agent to learn physically infeasible strategies that are ineffective when deploy…
Safe Reinforcement Learning for Legged Locomotion
Tsung-Yen Yang, Tingnan Zhang, Linda Luu +3
Designing control policies for legged locomotion is complex due to the under-actuated and non-continuous robot dynamics. Model-free reinforcement learning provides promising tools…